{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Correlation, Distance, and Clustering performance of yeast sample-specific networks\n",
    "\n",
    "We check how well the clustering is done. We create two labels:\n",
    "- labels_genetics: same integer for samples with the same mutation (for instance all gcn4 together)\n",
    "- labels_media: same integer for samples in the same medium (for instance all YPD together)\n",
    "\n",
    "Then we apply rand scores, adjuster rand, mutual information"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**The files generate here are then used for the general comparisons shown in the manuscript**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import scipy.stats as stats\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "from sklearn.metrics import roc_auc_score, roc_curve\n",
    "\n",
    "def get_roc_correlation(correlation_matrix, sample, check = 'genetics'):\n",
    "    \"\"\" Compute roc curve and auc between the correlation values of a sample and the true values\n",
    "    The true labels are based on whether one wants to check the genetics or the media\n",
    "    For instance if one wants to check the roc for gcn4_ypd, the true labels are the genetics\n",
    "    the true labels are gonna be 1 only for the other gcn4 samples and 0 for the rest.\n",
    "    \n",
    "    Args:\n",
    "        correlation_matrix (pd.DataFrame): correlation matrix between all networks\n",
    "        sample (str): sample name, needs to correspond to the column name of the correlation matrix\n",
    "        check (str, optional): Either genetics or media. Defaults to 'genetics'.\n",
    "\n",
    "    Returns:\n",
    "        tpr, fpr, th, roc_auc\n",
    "    \"\"\"\n",
    "    \n",
    "    y_score = (correlation_matrix.loc[sample,[i for i in correlation_matrix.columns if i!= sample]]).values\n",
    "    if check == 'genetics':\n",
    "        group = sample.split('_')[0]\n",
    "        y_true = np.array([1 if i.split('_')[0] == sample.split('_')[0] else 0 for i in correlation_matrix.columns if i!= sample])\n",
    "    elif check == 'media':\n",
    "        group = sample.split('_')[1]\n",
    "        y_true = np.array([1 if i.split('_')[1] == sample.split('_')[1] else 0 for i in correlation_matrix.columns if i!= sample])\n",
    "    else:\n",
    "        print('Pass either genetics or media to check')\n",
    "        \n",
    "    tpr, fpr, th = roc_curve(y_true, y_score)\n",
    "    auc =  roc_auc_score(y_true, y_score)\n",
    "    \n",
    "    return tpr, fpr, th, auc, group\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Generic for clustering\n",
    "\n",
    "from sklearn.metrics import adjusted_rand_score, rand_score, mutual_info_score, adjusted_mutual_info_score, completeness_score, fowlkes_mallows_score, homogeneity_score, v_measure_score\n",
    "\n",
    "def get_clustering_scores(labels_media, labels_genetics, predicted_labels, method_label):\n",
    "    \"\"\"Get clustering scores (both for media and genetics) for a given clustering method.\n",
    "    This function computes the following scores: adjusted_rand_score, adjusted_mutual_info_score, \n",
    "    completeness_score, fowlkes_mallows_score, v_measure_score, homogeneity_score\n",
    "\n",
    "    Args:\n",
    "        labels_media (np.array): array of labels for media\n",
    "        labels_genetics (np.array): array of labels for genetics\n",
    "        predicted_labels (np.array): predictions of the clustering method\n",
    "        method_label (str): label of the clustering method\n",
    "    \"\"\"\n",
    "    df_clustering = pd.DataFrame(columns = ['metric', 'value', 'check'])\n",
    "    scores = [adjusted_rand_score, adjusted_mutual_info_score, completeness_score, fowlkes_mallows_score]\n",
    "\n",
    "    for i in scores:\n",
    "        s_media = i(labels_media, predicted_labels)\n",
    "        s_genetics = i(labels_genetics, predicted_labels)\n",
    "        \n",
    "        media_temp = pd.DataFrame({'metric': i.__name__, 'value': s_media, 'check':'media'}, index = [0])\n",
    "        genetics_temp = pd.DataFrame({'metric': i.__name__, 'value': s_genetics, 'check':'genetics'}, index = [0])\n",
    "        \n",
    "        df_clustering = pd.concat([df_clustering, media_temp, genetics_temp], axis = 0, ignore_index=True)\n",
    "        #df_clustering.append({'metric': i.__name__, 'value': s_media, 'check':'media'}, ignore_index = True)\n",
    "        #df_clustering = df_clustering.append({'metric': i.__name__, 'value': s_genetics, 'check':'genetics'}, ignore_index = True)\n",
    "\n",
    "    df_clustering['clustering_method'] = method_label\n",
    "    return(df_clustering)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_pdist_test(dist, sample, check = 'genetics', test = 'mw'):\n",
    "    \"\"\" Compute roc curve and auc between the correlation values of a sample and the true values\n",
    "    The true labels are based on whether one wants to check the genetics or the media\n",
    "    For instance if one wants to check the roc for gcn4_ypd, the true labels are the genetics\n",
    "    the true labels are gonna be 1 only for the other gcn4 samples and 0 for the rest.\n",
    "    \n",
    "    Args:\n",
    "        distance matrix (pd.DataFrame): correlation matrix between all networks\n",
    "        sample (str): sample name, needs to correspond to the column name of the correlation matrix\n",
    "        check (str, optional): Either genetics or media. Defaults to 'genetics'.\n",
    "\n",
    "    Returns:\n",
    "        tpr, fpr, th, roc_auc\n",
    "    \"\"\"\n",
    "    \n",
    "    if check == 'genetics':\n",
    "        group = sample.split('_')[0]\n",
    "        y_true = np.array([1 if i.split('_')[0] == sample.split('_')[0] else 0 for i in dist.columns])\n",
    "        y_true[network_dist.columns.tolist().index(sample)] = 0\n",
    "        y_false = np.array([1 if i.split('_')[0] != sample.split('_')[0] else 0 for i in dist.columns])\n",
    "    elif check == 'media':\n",
    "        group = sample.split('_')[1]\n",
    "        y_true = np.array([1 if i.split('_')[1] == sample.split('_')[1] else 0 for i in dist.columns])\n",
    "        y_true[network_dist.columns.tolist().index(sample)] = 0\n",
    "        y_false = np.array([1 if i.split('_')[1] != sample.split('_')[1] else 0 for i in dist.columns])\n",
    "    else:\n",
    "        print('Pass either genetics or media to check')\n",
    "    \n",
    "    a = dist.loc[sample,:].values[y_true==1]\n",
    "    b = dist.loc[sample,:].values[y_false==1]\n",
    "        \n",
    "    if test == 'mw':\n",
    "        res = scipy.stats.mannwhitneyu(a, b, use_continuity=True, alternative='less')\n",
    "    elif test == 'tt':\n",
    "        res = scipy.stats.ttest_ind(a, b, equal_var=True, alternative='less')\n",
    "    else:\n",
    "        print('test not recognized, choose mw or tt')\n",
    "    \n",
    "    return( res.statistic, res.pvalue, group)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.spatial.distance import pdist, squareform\n",
    "def get_distance_matrix(df, method = 'euclidean'):\n",
    "    \"\"\"Get distance matrix from a dataframe\n",
    "    Args:\n",
    "        df (pd.DataFrame): dataframe to compute the distance matrix\n",
    "        method (str, optional): method to compute the distance matrix. Defaults to 'pearson'.\n",
    "\n",
    "    Returns:\n",
    "        pd.DataFrame: distance matrix\n",
    "    \"\"\"\n",
    "    dist = pdist(df.values, method)\n",
    "    \n",
    "    return pd.DataFrame(squareform(dist), index = df.index, columns = df.index)\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read weights dataframe\n",
    "#with open('../data/processed/sweet/GSE125162_all_pseudobulk_logcounts_weight.txt', 'r') as f:\n",
    "#    weights = pd.read_csv(f, sep = '\\t')\n",
    "## weights.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# random edges\n",
    "#random_sweet = pd.read_csv('../data/processed/sweet/GSE125162_all_pseudobulk_logcounts_sweet_networks_1krandom.txt',sep = '\\t')\n",
    "#random_sweet.head()\n",
    "\n",
    "#random_sweet_clean = random_sweet#.iloc[:,2:]\n",
    "#random_sweet_clean.columns = [nets_fn[int(i.split('_')[-1])].split('/')[-1][:-4] if i.startswith('network') else i for i in random_sweet_clean.columns]\n",
    "#random_sweet_clean.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load the network\n",
    "\n",
    "Here we assume that one already has a file where each row is an edge and every column is a sample.\n",
    "Otherwise one can add a piece of code to generate this object.\n",
    "\n",
    "Here is a list of networks that we want to test:\n",
    "1. BONOBO sparse: bonobo edges sparsified\n",
    "2. BONOBO top1k: bonobo top 1k edges by mean value\n",
    "3. SWEET sparse: Edges sparsified by filtering SWEET Zscores\n",
    "4. SWEET top1k: SWEET top 1k edges by mean value\n",
    "5. LIONESS top 1k: LIONESS top 1K edges by mean value\n",
    "\n",
    "\n",
    "Change the folder and file names accordingly to the files of interest.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cleaned and pseudobulked data\n",
    "params = {\n",
    "    'nonzero' : {'yeast_expression' : '../data/processed/pseudobulk_nonzero_logcounts.tsv',\\\n",
    "               'output_folder' : '../results/new_pseudobulk_nonzero/'},\\\n",
    "    'perc02' : {'yeast_expression': '../data/processed/pseudobulk_perc02_logcounts.tsv',\\\n",
    "              'output_folder' : '../results/new_pseudobulk_perc02/'}\n",
    "}\n",
    "params\n",
    "\n",
    "choice = 'nonzero'\n",
    "\n",
    "yeast_expression = params[choice]['yeast_expression']\n",
    "output_folder = params[choice]['output_folder']\n",
    "network = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'../results/new_pseudobulk_nonzero/'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output_folder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gene1</th>\n",
       "      <th>gene2</th>\n",
       "      <th>dal82_MinimalGlucose</th>\n",
       "      <th>dal81_YPDDiauxic</th>\n",
       "      <th>gln3_YPD</th>\n",
       "      <th>stp1_YPDRapa</th>\n",
       "      <th>dal81_MinimalEtOH</th>\n",
       "      <th>dal82_YPDDiauxic</th>\n",
       "      <th>WT(ho)_YPDRapa</th>\n",
       "      <th>gat1_YPD</th>\n",
       "      <th>...</th>\n",
       "      <th>dal80_YPEtOH</th>\n",
       "      <th>rtg1_CStarve</th>\n",
       "      <th>rtg3_YPDRapa</th>\n",
       "      <th>gln3_YPDRapa</th>\n",
       "      <th>rtg3_Urea</th>\n",
       "      <th>gcn4_YPDDiauxic</th>\n",
       "      <th>dal81_Urea</th>\n",
       "      <th>rtg1_Glutamine</th>\n",
       "      <th>rtg1_MinimalGlucose</th>\n",
       "      <th>dal82_YPD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NME1</td>\n",
       "      <td>HRA1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PWR1</td>\n",
       "      <td>YFL012W</td>\n",
       "      <td>0.921189</td>\n",
       "      <td>0.921188</td>\n",
       "      <td>0.887625</td>\n",
       "      <td>0.921189</td>\n",
       "      <td>0.921189</td>\n",
       "      <td>0.921188</td>\n",
       "      <td>0.921189</td>\n",
       "      <td>0.921189</td>\n",
       "      <td>...</td>\n",
       "      <td>0.921189</td>\n",
       "      <td>0.921189</td>\n",
       "      <td>0.921189</td>\n",
       "      <td>0.921189</td>\n",
       "      <td>0.921189</td>\n",
       "      <td>0.921188</td>\n",
       "      <td>0.921189</td>\n",
       "      <td>0.921189</td>\n",
       "      <td>0.921189</td>\n",
       "      <td>0.919331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>PWR1</td>\n",
       "      <td>YFR035C</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Q0045</td>\n",
       "      <td>Q0050</td>\n",
       "      <td>0.842767</td>\n",
       "      <td>0.894936</td>\n",
       "      <td>0.863078</td>\n",
       "      <td>0.861894</td>\n",
       "      <td>0.860010</td>\n",
       "      <td>0.870263</td>\n",
       "      <td>0.861966</td>\n",
       "      <td>0.863271</td>\n",
       "      <td>...</td>\n",
       "      <td>0.861396</td>\n",
       "      <td>0.861807</td>\n",
       "      <td>0.862441</td>\n",
       "      <td>0.860253</td>\n",
       "      <td>0.858888</td>\n",
       "      <td>0.872088</td>\n",
       "      <td>0.864021</td>\n",
       "      <td>0.860100</td>\n",
       "      <td>0.843846</td>\n",
       "      <td>0.863212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Q0045</td>\n",
       "      <td>Q0075</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.793110</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.806043</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.811269</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 134 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   gene1    gene2  dal82_MinimalGlucose  dal81_YPDDiauxic  gln3_YPD  \\\n",
       "0   NME1     HRA1                   NaN               NaN       NaN   \n",
       "1   PWR1  YFL012W              0.921189          0.921188  0.887625   \n",
       "2   PWR1  YFR035C                   NaN               NaN       NaN   \n",
       "3  Q0045    Q0050              0.842767          0.894936  0.863078   \n",
       "4  Q0045    Q0075                   NaN          0.793110       NaN   \n",
       "\n",
       "   stp1_YPDRapa  dal81_MinimalEtOH  dal82_YPDDiauxic  WT(ho)_YPDRapa  \\\n",
       "0           NaN                NaN               NaN             NaN   \n",
       "1      0.921189           0.921189          0.921188        0.921189   \n",
       "2           NaN                NaN               NaN             NaN   \n",
       "3      0.861894           0.860010          0.870263        0.861966   \n",
       "4           NaN                NaN          0.806043             NaN   \n",
       "\n",
       "   gat1_YPD  ...  dal80_YPEtOH  rtg1_CStarve  rtg3_YPDRapa  gln3_YPDRapa  \\\n",
       "0       NaN  ...           NaN           NaN           NaN           NaN   \n",
       "1  0.921189  ...      0.921189      0.921189      0.921189      0.921189   \n",
       "2       NaN  ...           NaN           NaN           NaN           NaN   \n",
       "3  0.863271  ...      0.861396      0.861807      0.862441      0.860253   \n",
       "4       NaN  ...           NaN           NaN           NaN           NaN   \n",
       "\n",
       "   rtg3_Urea  gcn4_YPDDiauxic  dal81_Urea  rtg1_Glutamine  \\\n",
       "0        NaN              NaN         NaN             NaN   \n",
       "1   0.921189         0.921188    0.921189        0.921189   \n",
       "2        NaN              NaN         NaN             NaN   \n",
       "3   0.858888         0.872088    0.864021        0.860100   \n",
       "4        NaN         0.811269         NaN             NaN   \n",
       "\n",
       "   rtg1_MinimalGlucose  dal82_YPD  \n",
       "0                  NaN        NaN  \n",
       "1             0.921189   0.919331  \n",
       "2                  NaN        NaN  \n",
       "3             0.843846   0.863212  \n",
       "4                  NaN        NaN  \n",
       "\n",
       "[5 rows x 134 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Choose network\n",
    "import sys\n",
    "\n",
    "\n",
    "\n",
    "network_name = 'bonobo_sparse'#'spcc_top1k'#'lioness_random'#'spcc_top1k'#'bonobo_top1k'#'bonobo_sparse'#'lioness_top1k'#'bonobo_sparse' #'sweet_top1k' \n",
    "results_folder = output_folder + 'clustering_performance/'+network_name+'/'\n",
    "if os.path.isdir(results_folder) == False:\n",
    "    if os.path.isdir(output_folder + 'clustering_performance/') == False:\n",
    "        os.mkdir(output_folder + 'clustering_performance/')\n",
    "    os.mkdir(results_folder)\n",
    "\n",
    "\n",
    "if network_name == 'sweet_sparse':\n",
    "    if choice == 'nonzero':\n",
    "        network = pd.read_csv(output_folder + '/GSE125162_all_pseudobulk_logcounts_sweet_networks_sparse.txt', sep = '\\t')\n",
    "    else:\n",
    "        network = pd.read_hdf(output_folder + 'sweet_sparse001.h5', key = 'sweet')\n",
    "    #network = pd.read_csv('../data/processed/sweet/GSE125162_all_pseudobulk_logcounts_sweet_networks_sparse.txt', sep = '\\t')\n",
    "elif network_name == 'sweet_top1k': \n",
    "    if choice == 'nonzero':\n",
    "        network = pd.read_csv(output_folder + '/GSE125162_all_pseudobulk_logcounts_sweet_networks_top1k.txt', sep = '\\t')\n",
    "    else:\n",
    "        network = pd.read_hdf(output_folder + 'sweet_top1k.h5')\n",
    "    #network = pd.read_csv('../data/processed/sweet/GSE125162_all_pseudobulk_logcounts_sweet_networks_top1k.txt', sep =\n",
    "    #'\\t')\n",
    "elif network_name == 'sweet_random': \n",
    "    if choice == 'nonzero':\n",
    "        pass\n",
    "        #network = pd.read_csv(output_folder + '/GSE125162_all_pseudobulk_logcounts_sweet_networks_top1k.txt', sep = '\\t')\n",
    "    else:\n",
    "        network = pd.read_hdf(output_folder + 'sweet_random1k.h5')\n",
    "    #network = pd.read_csv('../data/processed/sweet/GSE125162_all_pseudobulk_logcounts_sweet_networks_top1k.txt', sep = '\\t')\n",
    "elif network_name == 'bonobo_sparse': \n",
    "    network = pd.read_hdf(output_folder + 'bonobo_sparse_001.h5')\n",
    "elif network_name == 'bonobo_random': \n",
    "    network = pd.read_csv(output_folder + 'bonobo_random1k.csv')\n",
    "elif network_name == 'bonobo_top1k': \n",
    "    network = pd.read_csv(output_folder+'bonobo_top1k.csv')\n",
    "    #network = pd.read_csv('../../bonobo_paper_yeast/bonobo_top1k_from20230908_20240124/bonobo_top1k.csv')\n",
    "    #network = pd.read_csv('inferelator/bonobo_top1k_20240124/bonobo_top1k.csv')\n",
    "elif network_name == 'lioness_top1k': \n",
    "    network = pd.read_csv(output_folder+'lioness/networks/lioness_top1k.csv')\n",
    "    #network = pd.read_csv('../../bonobo_paper_yeast/data/networks/lioness_top1k.csv')\n",
    "elif network_name == 'lioness_random':\n",
    "    network = pd.read_csv(output_folder+'lioness/networks/lioness_random1k.csv')\n",
    "elif network_name == 'spcc_top1k':\n",
    "    network = pd.read_csv(output_folder+'networks/inferelator_spcc_top1k.csv')\n",
    "    #network = pd.read_csv('../../bonobo_paper_yeast/inferelator_spcc_top1k.csv')\n",
    "elif network_name == 'spcc_random':\n",
    "    network = pd.read_csv(output_folder+'networks/inferelator_spcc_random1k.csv')\n",
    "## Just for different bonobo thresholds\n",
    "elif network_name == 'bonobo_sparse_01':\n",
    "    if choice == 'nonzero':\n",
    "        network = pd.read_hdf(output_folder + 'bonobo_sparse_01.h5')    \n",
    "elif network_name == 'bonobo_sparse_005':\n",
    "    if choice == 'nonzero':\n",
    "        network = pd.read_hdf(output_folder + 'bonobo_sparse_005.h5')   \n",
    "elif network_name == 'spcc_top1k_old':\n",
    "    network = pd.read_csv(output_folder+'networks/old_spcc_top1k.csv') \n",
    "else:\n",
    "    sys.exit('Network (%s) not recognized' %network_name)\n",
    "network.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'../results/new_pseudobulk_nonzero/clustering_performance/bonobo_sparse/'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_folder"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Correlation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/bonobo-env/lib/python3.9/site-packages/seaborn/matrix.py:560: UserWarning: Clustering large matrix with scipy. Installing `fastcluster` may give better performance.\n",
      "  warnings.warn(msg)\n",
      "/opt/conda/envs/bonobo-env/lib/python3.9/site-packages/seaborn/matrix.py:560: UserWarning: Clustering large matrix with scipy. Installing `fastcluster` may give better performance.\n",
      "  warnings.warn(msg)\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2000x2000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compute correlation matrix\n",
    "# we need to select the data only, removing the index values\n",
    "sns.set_context('paper')\n",
    "network_corr = network.iloc[:,2:].dropna(axis = 0).corr()\n",
    "g1 = sns.clustermap(network_corr, cmap = 'jet', xticklabels = False, yticklabels = 1, figsize = (20,20))\n",
    "g1.figure.savefig(results_folder + 'network_correlation.pdf', bbox_inches = 'tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Compute ROC between Peearson and binary labels by both media and genetics\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'../results/new_pseudobulk_nonzero/clustering_performance/bonobo_sparse/'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_folder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_92391/3199061701.py:8: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
      "  corr_performance = pd.concat([corr_performance, df_temp], axis = 0)\n"
     ]
    }
   ],
   "source": [
    "# Let's check the individual networks and their correlation for genetics\n",
    "corr_performance = pd.DataFrame(columns = ['sample','auc', 'check', 'tpr', 'fpr', 'th', 'group'])\n",
    "for s in network.columns[2:]:\n",
    "    for ccc in ['genetics', 'media']:\n",
    "        tpr, fpr, th, auc, group = get_roc_correlation(network_corr, s, check = ccc)\n",
    "        # For each sample compute the ROC between the correlation values and the true values\n",
    "        df_temp = pd.DataFrame(data = [[s, auc, ccc, tpr, fpr, th, group]], columns = ['sample', 'auc', 'check', 'tpr', 'fpr', 'th', 'group'])\n",
    "        corr_performance = pd.concat([corr_performance, df_temp], axis = 0)\n",
    "\n",
    "corr_performance['network'] = network_name\n",
    "\n",
    "# Save network\n",
    "corr_performance.to_csv(results_folder + 'network_correlation_performance.csv', index = False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1120.25x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "media_color = '#d6604d'\n",
    "genetics_color = \"#4393c3\"\n",
    "\n",
    "sns.set_theme(style=\"whitegrid\")\n",
    "\n",
    "g1 = sns.catplot(data = corr_performance.sort_values(by = 'check'), x = 'group', y = 'auc', hue = 'check', \n",
    "                 kind = 'violin', \n",
    "                 aspect = 2, \n",
    "                 hue_order=['media', 'genetics'], palette = [media_color, genetics_color])\n",
    "# rotate xtick labels\n",
    "g1.set_xticklabels(rotation=90)\n",
    "# add horizontal grid lines\n",
    "g1.ax.yaxis.grid(True)\n",
    "# Add y axis line for 0.5\n",
    "g1.ax.axhline(0.5, ls = '--',lw= 2, color = 'red', alpha = 0.5)\n",
    "# Add title\n",
    "g1.ax.set_title(network_name)\n",
    "# Save figure\n",
    "g1.fig.savefig(results_folder + 'network_correlation_performance.pdf', bbox_inches = 'tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "def make_pretty(styler):\n",
    "    styler.set_caption(\"Performance of the correlation between networks\")\n",
    "    #styler.format(rain_condition)\n",
    "    #styler.format_index(lambda v: v.strftime(\"%A\"))\n",
    "    styler.background_gradient(axis=None, vmin=-1, vmax=1, cmap=\"RdBu_r\")\n",
    "    return styler\n",
    "\n",
    "corr_performance.groupby(['check', 'group']).describe().iloc[:,1:].\\\n",
    "    style.pipe(make_pretty).\\\n",
    "    to_latex(results_folder + 'network_correlation_performance_describe.tex', convert_css=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Distance performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_92391/3347740435.py:13: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
      "  dist_performance = pd.concat([dist_performance, df_temp], axis = 0)\n"
     ]
    }
   ],
   "source": [
    "# Let's check the individual networks and their distance for genetics and media\n",
    "import scipy\n",
    "dist_performance = pd.DataFrame(columns = ['sample','test', 'check', 'stat', 'pval', 'group', 'method'])\n",
    "\n",
    "for method in ['euclidean', 'cosine']:\n",
    "    network_dist = get_distance_matrix(network.iloc[:,2:].dropna(axis = 0).T, method = method)\n",
    "    for s in network.columns[2:]:\n",
    "        for ccc in ['genetics', 'media']:\n",
    "            for test in ['mw']:\n",
    "                stat, p , group = get_pdist_test(network_dist, s, check = ccc, test = test)\n",
    "                # For each sample compute the ROC between the correlation values and the true values\n",
    "                df_temp = pd.DataFrame(data = [[s, test, ccc, stat, p,  group, method]], columns = ['sample','test', 'check', 'stat', 'pval', 'group', 'method'])\n",
    "                dist_performance = pd.concat([dist_performance, df_temp], axis = 0)\n",
    "\n",
    "\n",
    "dist_performance['network'] = network_name\n",
    "\n",
    "# Save network\n",
    "dist_performance.to_csv(results_folder + 'network_distance_performance.csv', index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sample</th>\n",
       "      <th>test</th>\n",
       "      <th>check</th>\n",
       "      <th>stat</th>\n",
       "      <th>pval</th>\n",
       "      <th>group</th>\n",
       "      <th>method</th>\n",
       "      <th>network</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>dal82_MinimalGlucose</td>\n",
       "      <td>mw</td>\n",
       "      <td>genetics</td>\n",
       "      <td>576.0</td>\n",
       "      <td>4.024412e-01</td>\n",
       "      <td>dal82</td>\n",
       "      <td>euclidean</td>\n",
       "      <td>bonobo_random</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>dal82_MinimalGlucose</td>\n",
       "      <td>mw</td>\n",
       "      <td>media</td>\n",
       "      <td>430.0</td>\n",
       "      <td>2.841730e-02</td>\n",
       "      <td>MinimalGlucose</td>\n",
       "      <td>euclidean</td>\n",
       "      <td>bonobo_random</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>dal81_YPDDiauxic</td>\n",
       "      <td>mw</td>\n",
       "      <td>genetics</td>\n",
       "      <td>614.0</td>\n",
       "      <td>5.328136e-01</td>\n",
       "      <td>dal81</td>\n",
       "      <td>euclidean</td>\n",
       "      <td>bonobo_random</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>dal81_YPDDiauxic</td>\n",
       "      <td>mw</td>\n",
       "      <td>media</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.428350e-08</td>\n",
       "      <td>YPDDiauxic</td>\n",
       "      <td>euclidean</td>\n",
       "      <td>bonobo_random</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>gln3_YPD</td>\n",
       "      <td>mw</td>\n",
       "      <td>genetics</td>\n",
       "      <td>859.0</td>\n",
       "      <td>9.863068e-01</td>\n",
       "      <td>gln3</td>\n",
       "      <td>euclidean</td>\n",
       "      <td>bonobo_random</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>rtg1_Glutamine</td>\n",
       "      <td>mw</td>\n",
       "      <td>media</td>\n",
       "      <td>189.0</td>\n",
       "      <td>4.719108e-05</td>\n",
       "      <td>Glutamine</td>\n",
       "      <td>cosine</td>\n",
       "      <td>bonobo_random</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>rtg1_MinimalGlucose</td>\n",
       "      <td>mw</td>\n",
       "      <td>genetics</td>\n",
       "      <td>638.0</td>\n",
       "      <td>6.142344e-01</td>\n",
       "      <td>rtg1</td>\n",
       "      <td>cosine</td>\n",
       "      <td>bonobo_random</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>rtg1_MinimalGlucose</td>\n",
       "      <td>mw</td>\n",
       "      <td>media</td>\n",
       "      <td>565.0</td>\n",
       "      <td>2.164499e-01</td>\n",
       "      <td>MinimalGlucose</td>\n",
       "      <td>cosine</td>\n",
       "      <td>bonobo_random</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>dal82_YPD</td>\n",
       "      <td>mw</td>\n",
       "      <td>genetics</td>\n",
       "      <td>558.0</td>\n",
       "      <td>3.434540e-01</td>\n",
       "      <td>dal82</td>\n",
       "      <td>cosine</td>\n",
       "      <td>bonobo_random</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>dal82_YPD</td>\n",
       "      <td>mw</td>\n",
       "      <td>media</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.211450e-08</td>\n",
       "      <td>YPD</td>\n",
       "      <td>cosine</td>\n",
       "      <td>bonobo_random</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>528 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  sample test     check   stat          pval           group  \\\n",
       "0   dal82_MinimalGlucose   mw  genetics  576.0  4.024412e-01           dal82   \n",
       "0   dal82_MinimalGlucose   mw     media  430.0  2.841730e-02  MinimalGlucose   \n",
       "0       dal81_YPDDiauxic   mw  genetics  614.0  5.328136e-01           dal81   \n",
       "0       dal81_YPDDiauxic   mw     media    2.0  2.428350e-08      YPDDiauxic   \n",
       "0               gln3_YPD   mw  genetics  859.0  9.863068e-01            gln3   \n",
       "..                   ...  ...       ...    ...           ...             ...   \n",
       "0         rtg1_Glutamine   mw     media  189.0  4.719108e-05       Glutamine   \n",
       "0    rtg1_MinimalGlucose   mw  genetics  638.0  6.142344e-01            rtg1   \n",
       "0    rtg1_MinimalGlucose   mw     media  565.0  2.164499e-01  MinimalGlucose   \n",
       "0              dal82_YPD   mw  genetics  558.0  3.434540e-01           dal82   \n",
       "0              dal82_YPD   mw     media    0.0  2.211450e-08             YPD   \n",
       "\n",
       "       method        network  \n",
       "0   euclidean  bonobo_random  \n",
       "0   euclidean  bonobo_random  \n",
       "0   euclidean  bonobo_random  \n",
       "0   euclidean  bonobo_random  \n",
       "0   euclidean  bonobo_random  \n",
       "..        ...            ...  \n",
       "0      cosine  bonobo_random  \n",
       "0      cosine  bonobo_random  \n",
       "0      cosine  bonobo_random  \n",
       "0      cosine  bonobo_random  \n",
       "0      cosine  bonobo_random  \n",
       "\n",
       "[528 rows x 8 columns]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dist_performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABEMAAAHjCAYAAADIeNGrAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8g+/7EAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzdd3hURfvw8e/Zkt6zKSQhhJYQCAQQApH6iAryWGgKWFDxFRFBBETExwIWEFRQQQXFgv4sqAiiWFAERemdQIAQWnrb9LLZct4/YlbWhB4IJPfnutbszs6Zc8+CmeU+M3MUVVVVhBBCCCGEEEIIIRoJTX0HIIQQQgghhBBCCHE5STJECCGEEEIIIYQQjYokQ4QQQgghhBBCCNGoSDJECCGEEEIIIYQQjYokQ4QQQgghhBBCCNGoSDJECCGEEEIIIYQQjYokQ4QQQgghhBBCCNGoSDJECCGEEEIIIYQQjYokQ4QQQgghhBBCCNGoSDJEiKvUzp07WbBgAUVFRZf0PJ9++inffPPNJT3H1SA1NZWoqCi2bNlS36HYHTlyhAULFpCamlrjvXvuuYebb775ksdQ/bk0hr8jW7ZsqfF3YMGCBURFRZ3T8ddddx1PPvnkpQpPiDolY8zlJWNM7RrDGNMY+ijElUqSIUJcpXbt2sXChQsv+RfVzz//nBUrVlzSc4gLc+TIERYuXEhaWlp9h9Jo3X777Sxbtqy+wxCizskYI2SMuTwCAwNZtmwZffv2re9QhGh0dPUdgBBCCHG1Cg4OJjg4uL7DEEIIcZVycnKiY8eO9R2GEI2SzAwR4iq0YMEC5s6dC0C/fv2IioqqMb32hx9+YPjw4XTs2JFOnTrxwAMPcODAAYd2UlJSmDRpEj179iQmJoZrr72We++9l8TERKBqWn9SUhJbt261n+O66667bP287rrreOihh1i3bh2DBg2iQ4cO3HTTTaxbtw6Ab775hptuuomOHTsybNgw9u3bZz92/fr1REVFsXfvXnvZzz//TFRUFGPGjHE4zy233MKECROuiLir7du3j7FjxxIXF0f79u0ZNGgQP/zwg/39b775hokTJwIwatQo+5/Pv6fZ7t27lzvvvJPY2Fj69evHu+++i81mc6iTnp7O448/Tnx8PDExMdx000188MEHNeplZWUxceJEOnXqxDXXXMNjjz1Gbm5unXxm56ukpIQ5c+Zw3XXXERMTQ69evXjppZcoKyuz1znT1OOoqCgWLFjgUJacnMzkyZO59tpriYmJoW/fvjzxxBNUVlaeNo7alsmYzWbmzp1Ljx49iI2NZeTIkQ5/D0+Vk5PDs88+S+/evYmJieG6665j4cKFWCwWh3oLFy7k9ttvJy4ujs6dOzN48GC++uorVFV1qFf9d++PP/5g8ODBdOjQgQEDBvD111+ftg9C/JuMMTLGNPYxpqioiJdffpl+/foRExNDfHw8Dz74IMnJyfY6BQUFzJgxg169ehETE0O/fv2YP39+jTHjxx9/5Pbbb+eaa66xf07Tp0+3v1/bWFU9tiQlJTF58mSuueYarr32WqZPn05xcbFD+6qq8umnn3LbbbfRoUMHunbtyqOPPkpKSsol+nSEaDhkZogQV6Hbb7+dwsJCPvnkExYuXEhAQAAArVq1AmDRokW8/vrrDBkyhIcffhiz2cz777/PXXfdxVdffWWv9+CDD2Kz2Zg6dSohISHk5+eza9cu+7TohQsX8uijj+Lp6clzzz0HVF3BOBOr1VrjH2i10Wg0aDRnz8cePHiQefPmMXbsWDw8PHjrrbeYMGECY8aMYdOmTUyePBlFUXjllVcYO3Ysa9euxcXFha5du6LX69m0aRMdOnQAYOPGjbi4uLBt2zbMZjN6vZ68vDySkpIYOXLkWWM5HxcaN8DmzZv5f//v/xEbG8uMGTPw9PTkhx9+YNKkSVRUVDBkyBD69u3L5MmTmTdvHs8++yzt2rUDIDw83B5DTk4OU6dO5f7772f8+PH88ssvvPbaawQGBjJo0CAAjEYjI0aMwGw2M3HiREJDQ1m/fj1z5szh5MmTzJgxA4CKigruv/9+srOzmTJlChEREaxfv55Jkyad82fy73/gn45Wq0VRlNO+X15ezt13301mZiZjx461f2F88803OXz4MB999NEZj6/NwYMHGTlyJL6+vjz66KM0a9aMnJwcfvvtNyorK8/69/5UzzzzDCtXrmT06NH06NGDpKQkxo8fT2lpqUO9nJwcbr/9djQaDY888gjh4eHs2rWLd955h7S0NGbPnm2vm5aWxvDhwwkJCQFg9+7dvPjii2RlZTF+/PgafZkzZw4PPvggBoOBr776iv/97380a9aMrl27ntfnIhonGWNkjGnMY0xJSQl33nknaWlp9s+prKyMbdu2kZOTQ8uWLTGZTIwaNYqUlBQmTJhAVFQU27dv59133yUxMZF3330XqFpuNmnSJAYOHMj48eNxdnYmPT2dzZs3n1OsEyZMYODAgQwbNozDhw/z2muvATiMD88++ywrVqzgnnvu4fHHH6ewsJC33nqLESNG8O2332IwGM7pXEI0SqoQ4qq0ZMkSNTIyUk1JSXEoT09PV9u2bau+8MILDuUlJSVqjx491IkTJ6qqqqpGo1GNjIxUP/roozOe57///a969913n3Ncd999txoZGXnWx7Rp087a1n/+8x+1Q4cOamZmpr0sMTFRjYyMVHv06KGWlZXZy3/55Rc1MjJSXbt2rb1s5MiR6qhRo+yvb7jhBnXOnDlqmzZt1K1bt6qqqqqrVq1SIyMj1WPHjp0xlpSUFDUyMlLdvHnzJY97wIAB6qBBg1Sz2ezQ7kMPPaT26NFDtVqtqqqq6o8//njamKr/HPbs2eNQPnDgQHX06NH216+++mqt9Z577jk1KipKPXr0qKqqqvrZZ5+pkZGR6q+//upQ7+mnn1YjIyPV5cuXn/Ezqf78zuVxts948eLFaps2bdS9e/c6lP/0009qZGSkun79eodz1hZbZGSk+uabb9pfjxo1Su3SpYual5d32vNu3ry5RnxvvvmmGhkZaX995MgRNTIyUp01a5bDsdV/z079e//MM8+oHTt2VNPS0hzqvv/++2pkZKSalJRUaxxWq1U1m83qwoUL1bi4ONVms9nf+89//qO2b9/eoc2Kigo1Li5OfeaZZ07bNyH+TcYYGWMa6xizcOFCNTIyUv3rr79OW+fzzz9XIyMj1R9++MGh/N1331UjIyPVP//8U1XVf36fFxUVnTX2U/tYPba89957DnVnzJihtm/f3v57f9euXWpkZKT6wQcfONTLyMhQO3TooM6dO/eMfRWisZOZIUI0MH/++ScWi4XbbrvN4SqJs7MzXbt2tU9z9vHxITw8nPfffx+bzUa3bt1o06bNOV1JO5OZM2fWuAJeG19f33NqLzo6mqCgIPvrFi1aANCtWzdcXV3t5S1btgSqpuNWi4+P591336WiooK8vDxOnDjBf//7XzZv3sxff/1F165d2bhxIyEhIURERJxTPOfqQuM+ceIER48eZdq0aYDjla7evXuzbt06jh07Zj/uTAICAuxXLKtFRUXZp6hD1RXCVq1a1ag3ZMgQPv/8czZv3kzz5s3ZsmUL7u7u9OvXz6HezTffzJdffnnWWAIDA895qUbz5s3P+P66deto3bo10dHRDp9Pz549URSFrVu30qdPn3M6F1TNNNm2bRvDhg3Dz8/vnI+rTfX/X7fccotD+U033VTjTjLr16+nW7duBAYG1vhznjNnDlu3brVfYd+0aROLFy9m3759lJSUOLSTl5fncOUvOjraPoMEqv7fj4iIcPh/Q4gLJWOMjDHVGuoYs2HDBiIiIrj22mtPW2fz5s24ubkxYMAAh/IhQ4bw6quvsmnTJnr06EH79u0BeOyxxxg6dCjXXHONw5/b2fx72VhUVBQmk8n+e3/dunUoisKtt97q8GdpMBho06YNW7duPedzCdEYSTJEiAamen3tsGHDan2/+ouooih89NFHvPXWWyxZsoSXX34ZHx8fbrnlFh577DE8PDwu6PzNmjU75ynM58Lb29vhdfUU6n+X6/V6AEwmk70sPj6ehQsXsmPHDtLT0/H19aVt27bEx8ezadMmHnvsMTZv3kx8fPw5xXI+LjTu6j+/OXPmMGfOnFrbzs/PP6cYfHx8apQ5OTk5fEYFBQWEhobWqBcYGGh/v/pnbVNtz3X6rZOTE9HR0edUV6vVnvH96n90VE/b/rdz/XyqFRUVYbVaz+sL6ulUf17Vywqq6XS6Gn8eeXl5rFu37qz92Lt3Lw888ABxcXG88MILBAcHo9fr+fXXX1m0aBEVFRUOx53Ln7sQF0rGGBljqjXUMcZoNNKkSZMz1qmO99/Lbfz9/dHpdPZ+de3albfeeotPPvmEadOmUVlZSevWrRk7duw53Zr4359x9Z9z9e/9vLw8VFU9beKmadOmZz2HEI2ZJEOEaGCqr4a9+eabDleHaxMaGsqsWbMAOHbsGD/++CMLFy6ksrKS559//oLOf999953TlYjBgwfz8ssvX9A5zlVsbCxubm5s3LiRtLQ04uPjURSF+Ph4PvzwQ/bu3Ut6evoZr/5cbtV/fg899BA33HBDrXXOdlXrfPj4+JCTk1OjPDs72yEeHx+fWjcBPdfN7VJTU2tc8Tudjz/+mG7dup32fV9fX5ydne1/d2t7H6quVAM1NrP79xd9b29vtFotWVlZ5xTfmVR/cc3JyXFIrlgsFvuX41PjjIqK4rHHHqu1rep/LKxevRqdTsfixYvtfQL49ddfLzpeIc6XjDH/kDHm7K7GMcbPz4/MzMwztuHj48OePXtQVdUhIZKXl4fFYnGYmXT99ddz/fXXU1lZye7du1m8eDFTpkwhNDSUTp06nVPMp+Pr64uiKHz66ae17rdzPvtdCdEYSTJEiKtU9QD376u9PXv2RKfTcfLkSfr373/O7TVv3pxx48axZs0ahzsCODk51bjyfCZ1PYX5Yuj1evs05YyMDKZMmQJAly5d0Gq1vPHGG/YvrleKFi1aEBERwcGDB5k8efIZ6/77CtGFiI+PZ/Hixezfv99hhsLKlStRFMX+hbFbt278+OOPrF271uEL5/fff39O56nLKcx9+/Zl8eLF+Pj4nPGql8FgwNnZmUOHDjmUr1271uF19WaIP/30E4899thFLZWp/ry+++47YmJi7OU//vhjjc39+vbty++//054eHiNq7mnUhQFrVbrcKW7oqKCVatWXXCcQpyNjDFnJ2PM2V2NY0yvXr1488032bRp02n/7OLj4/nxxx/59ddfHZJKK1eutL//b05OTsTFxeHl5cWff/7JgQMHLjoZ0rdvX959912ysrIYOHDgRbUlRGMkyRAhrlKRkZEALF26lMGDB6PT6WjevDlhYWE8+uijvP7666SkpNC7d2+8vLzIzc1l3759uLq68uijj3Lw4EFeeOEFBgwYQLNmzdDr9WzevJlDhw453BYwMjKS1atX88MPPxAWFoazs3ONW4meqnrd8pUiPj7efnWw+uqci4sLnTp14s8//yQqKgp/f//6DLGGmTNn8uCDD/LAAw8wePBggoKCKCwsJDk5mf379/Pmm28C0Lp1awC+/PJL3N3dcXZ2Jiws7Lz+EXDfffexcuVKHnroIR599FFCQkJYv349n332GSNHjrR/aRw0aBAfffQR06ZNY9KkSTRr1ozff/+dP//885zO4+TkZF87fbHuvfde1qxZw9133819991HVFQUNpuNjIwM/vzzT0aPHk1sbKx9HfXy5csJDw+nTZs27N27t9Yv19OnT2fkyJHccccdjBkzhvDwcPLy8vjtt9+YOXPmOU/pb9myJbfeeitLly5Fp9Nx7bXXkpSUxPvvv1+jjUcffZSNGzcyYsQI7rnnHpo3b05lZSWpqan88ccfzJw5k+DgYPr06cOHH37IlClTGD58OAUFBbz//vtyxU9cUjLGnBsZY87sah1jfvzxR8aNG8eYMWPo0KEDFRUVbNu2jb59+9K9e3cGDRrEp59+yrRp00hLSyMyMpIdO3awePFi+vTpY/+78MYbb5CZmUl8fDzBwcEUFRXx8ccfo9friYuLu+hYr7nmGoYPH85TTz1FQkICXbt2xdXVlZycHHbs2EFkZCR33nnnRZ9HiIZKkiFCXKW6devGQw89xIoVK/jqq6+w2Wz2qZ8PPfQQLVu25OOPP2b16tVUVlYSEBBATEyM/fZ+AQEBhIeH89lnn9mngzZt2pRp06Zxzz332M8zYcIEcnJyePrppyktLSU0NJTffvutXvp8IaqvzkRERDisW7722mvZsmXLFTV9uVr37t356quvWLRoEbNmzaKoqAgfHx9atmzJTTfdZK/XtGlTnnrqKT7++GNGjRqF1Wpl9uzZDBky5JzP5efnxxdffMFrr73Ga6+9RmlpKWFhYfbbJVZzdXXl448/5qWXXuLVV19FURR69uzJvHnzGDFiRJ32/2zc3Nz49NNPeffdd1m2bBmpqam4uLjQpEkTrr32Woc/5+pNS5csWUJZWRndunVj0aJFNTala9OmDV9//TVvvvmm/XMICAige/fu5510eOmllzAYDKxYsYJPPvmE6OhoFixYUOMqbPWVzLfffpv333+frKws3N3dCQ0NpVevXnh5eQFVf4dnzZrFe++9x9ixYwkKCuKOO+7Az8+P//3vfxfyEQpxVjLGnBsZY87sahxjPDw8+Oyzz1iwYAFffvklb731Fl5eXrRv35477rgDqFqG+fHHHzN//nyWLFlCfn4+QUFBjB492uF257GxsSQkJPDqq69iNBrx8vIiJiaGjz76yJ5suljPP/88sbGxLFu2jM8//xybzUZgYCCdO3eusXGtEMKRop7LLlRCCNHIVa9HPttaYyGEEOJ8yRgjhBCX38Xd30wIIYQQQgghhBDiKiPJECGEEEIIIYQQQjQqkgwRQgghhBBCCCFEoyJ7hgghhBBCCCGEEKJRkZkhQgghhBBCCCGEaFQkGSIavEOHDnHo0KH6DkMIIcQVSsYJIYQQovGRZIho8CorK6msrKzvMDCZTOzYsQOTyVTfodQJ6c+VqyH1BaQ/V7qG0J8rZZyAhvF5VmtIfQHpz5WuIfWnIfUFGl5/RMMhyRAhLhOr1erw82on/blyNaS+gPTnStfQ+lPfGtLn2ZD6AtKfK11D6k9D6gs0vP6IhkOSIUIIIYQQQgghhGhUdPUdgBBCiPNTUVFBTk4O5eXl6HQ6fH198fHxQVGU+g5NCCGEEEKIq4IkQ8RZGY1G4uPjmT17NkOGDDmnY7755humT5/Opk2b8PPzs7fz+uuvs2HDBoxGI6GhoQwfPpxRo0bZ/xG3d+9evvjiC7Zv305WVhaBgYH069eP8ePH4+Hhccn6KMSVzGq1kpCQwObNm9m7dy9paWk16nh4etKubVvi4uKIjY2thyiFEELUF6vVSnJyMomJiRw9epT09HTy8/OpMJnQabV4enoSFBREREQEUVFRtGvXrr5DFkKIeifJEHHZTJgwgRMnTjBp0iRCQkLYuHEjs2bNwmazcf/99wPw448/cuzYMUaPHk3z5s05fvw4b7zxBjt37uSLL75Ao5GVXaLxSEtLY8GCBSQlJTmus1U0oCjA3zNBVJWS4hK2bNnCli1b0On1tGvbFldXV9q2bVsvsQshhLi0ysvL2bFjB3/99Rfbt2/HbDb/q4ZSNUyoUFBQQEpKCtu3bwdAo9US3aYNYWFhBAYG4ubmdtnjF0KI+ibJEHFZZGZmsn37dmbNmsXQoUMBiI+P5+DBg/zwww/2ZMiDDz5on0kC0K1bNwwGA+PGjWP79u3ExcXVS/xCXC7Vs0BWr17N1q1ba60z5+XZAEybNq3W9y0WK3v27GHPnj20bduWm2++mbi4OHQ6+ZUvhBBXs5ycHHbu3MnWrVvZs2cPFovltHXnzHkZqGWs0Ltgc/Fg//797N+/n59//pnIyEji4+Pp2rUroaGhsuxSCNEoyDdjUcOXX37JokWLyMvLo2PHjkyaNMnh/ZUrV7Js2TKSk5NRVZU2bdowdepUOnTocNo2q69WeHp6OpR7eXmRn59vf31qIqRa9ZXt7OzsC+6TEJeb2WymrKyMyspKrFYriqKg1+txcnLCyckJrVaLzWajvLyc3NxcTp48yf79+9m+fbvD/xMXRLVBYEsUi4kDBw5w4MABvLy8uPbaa+nSpQtt27aVq4BCCHEFU1WVkpIS0tPTOX78OElJSezfv5+MjIx/Knn4g9YJCjNO31BtzBUoIdFoYm5EzTuBmnOMw0lJHD58mKVLlxIQEED79u1p06YNLVq0ICwsDBcXl7rtoBBCXAEkGSIcrFu3jmeeeYYhQ4YwcOBAEhISmDx5skOd1NRUBg0aRHh4OJWVlXz//ffcddddrFq1iubNm9fabtOmTenRoweLFi0iIiKCkJAQNm3axC+//MLzzz9/xpiqp3S2bNmybjopRB3Iz89nyZIl7N+/v8Z75eXlmEymC2tY74IS0gY1Px3Kiy48wNwTKO1vRNMqHlv6QYqyj/DTTz/x008/oSgKHh4eaDQaoqKiGDt2bK2JSCGEaOx27drF+++/T2lpKaqqYrFY0Ol09pkTqqraf1ZWVmK2WLBaLPZyjUaDTqdDr9ej0+tRAEVRUFW1xuyLU9syVVZSUV7uGIzOCcW/GYpfKIp/OGp5EbZ9ay6oX2rKXqx5J0GrRxPRGU3b61DzUlDzUsgpSOe3337jt99+s9f38PBAq9WiKArOzs7ceeed9OnT54LOLYQQVwpJhggH77zzDl26dGH27Kpp+L169aK8vJzFixfb64wfP97+3Gaz0aNHD/bt28eKFStqJE5OtXDhQiZNmsQtt9wCVH0ZmDp1KoMGDTrtMUVFRcyfP5/4+Hiio6MvsndC1J0tW7bw119/1WGLCkpYOzRNO6A4u2HZvuLimrNZUFP3oWnfH22rbqhNY7Alb0XNrprRVVxcDMDWrVuJi4vj+uuvr4M+CCFEw/LBBx+Qmpp6wcfbbDYqKyuprKy88CD0LlUzOZpEobj8s5m8LekvsJ1+mcyZA7NCSR6AfaxQgltDcGtUUxlqxkFsaQfAXAFASUmJw+H/93//J8kQIcRVT5Ihws5qtbJ//36mTp3qUN6/f3+HZEhycjLz5s1j165d5OXl2cuPHz9+2rZVVWX69OkcP36cV199laCgILZu3cr8+fPx8vLi9ttvrzWexx9/nPLycl566aWL76AQdahv375UVlaSmZlpLysvL6ewsJDi4mKKi4spLS2jstKE1WZDoyjodDqcnJxwdna2L5MpKyujuLgYVVVRUxOwpiaAdzA4u9u/qF4QZ3eU0BhsuSdQ0xNRjWlA1VVHf4OB5hERGAwG/P396dGjx0V+GkII0TA98cQT/PLLL1gsFiwWC/n5+fj4+KDX6zGbzVRUVGAymSgvL6+aGWI2Y6llZkj1EklnZ2ecnZ1xcnJCr9c7bAyvqioVFRWUlpZSUFBAdnY2BQUFYK5APbEL64ld4OqF4heG4h+OEtoOtcQIptLz75jeFZzdQKtHCY1BLcquGi/yUqDU6FA1ICCA4OBgDAYDrq6uaDQaevbseTEfqxBCXBEkGSLsjEYjFoulxnR5g8Fgf15SUsLo0aPx8/PjySefJCQkBGdnZ55++ukzLgtYv349P/30E99++y1t2rQBIC4ujqKiIubOncvQoUNr3CnmmWeeYdu2bSxdupTQ0NA67KkQF8/FxYVbb721TtqqrKwkJSWF/fv3s23bNhISEv7+Iv33bQDOl5s3SmBrbIf/hIqqGSBt2rShd+/eXHPNNQQFBdVJ3EII0dA1bdqU0aNHA1BWVkZiYiLR0dGXbd+lsrIy+54hBw4cYN++fZSlHUBNOwA6J/AJqap4PgkRNx80sf9FKS/AlnMc9dAf2P4+3snJiZjOnWnfvj1RUVE0b94cV1fXS9AzIYSof5IMEXZ+fn7odDqMRscrArm5ufbnu3fvJjMzk8WLF9uTGgDFxcUEBweftu0jR46g1WqJiopyKI+Ojmbp0qUUFBQ4JGHmzp3Lt99+y9tvv33GjVmFaAicnJxo2bIlLVu25NZbbyU3N5dff/2Vn376mYKCvzdTVTSg0YJGy7SnZ1SV6U/9gqpWfTFWtFBRhHp8O05OTlw3YAD//e9/adq06eXulhBCiIvk5uZG27Ztadu2LbfddhtWq5WDBw+ybds2Nm/eTGbm8X8qa3VV+XONBhTNv8YKFfTO4OQOWj22bV+DpeoiloeHB9169qN79+506NABZ2fny9xLIYSoH5IMEXZarZa2bdvyyy+/cN9999nLf/75Z/vzioqqtaN6vd5etnPnTtLS0mjduvVp2w4NDcVqtZKYmGi/OwxAQkICbm5u+Pr62svee+89PvjgA2bPni3rUUWjZDAYGDFiBMOGDWPLli2sWbOGvXv3olrNYDWDk1vVVGmdE9hsqKZSKC+0r+0OCgqiffv23HHHHQQGBtZzb4QQQtQVrVZLu3btaNeuHffeey8nTpxg69atbN++naSkpKpZhbZTDlAU+HvJDuYKKCsEIDAwiI4dYwkMDKR///417vYnhBCNgSRDhIOxY8cybtw4pk+fbr+bzPfff29/v2PHjri5uTFz5kzGjBlDVlYWCxcuPOu0+z59+hAaGsrEiRN55JFHCAoKYvPmzXz++eeMHj3avqP6d999x6uvvsp///tfmjdvzu7du+1tBAcHn3H2iRANjU6no0ePHvTo0YO8vDy2bNnC3r17OXr0KDk5WfY16e7u7jSLbkPbtm2Ji4sjNDSUgwcP4uHhcZYzCCGEuFopikJERAQRERHccccdlJaWcujQIZKTk0lPT6egoACTyYRWq8XT05OgoCAiIiJo06YNQUFB9mU/Wq22vrsihBD1QpIhwkG/fv2YOXMmixYtYvXq1cTGxvLaa68xYsQIoOqK9RtvvMHcuXMZN24cERERzJgxgyVLlpyxXXd3d5YuXcr8+fOZN28ehYWFhIWFMXnyZO699157veq7c6xevZrVq1c7tDF+/HgmTJhQxz0W4urg7+/PwIEDGThwIFB1hwKTyWS/ZeOpysrK6iNEIYQQ9cjd3Z3OnTvTuXPn+g5FCCGuCpIMETWMGDHCnvyodujQIfvz3r1707t3b4f3/72cZciQIQwZMsShrGnTpsybN++M53755Zd5+eWXLyRsIRoVjUYjm9oJIYQQQghxgTRnryKEEEIIIYQQQgjRcEgyRAghhBBCCCGEEI2KJEOEEEIIIYQQQgjRqEgyRAghhBBCCCGEEI2KJENEnUlNTWXBggVkZWVd0PEJCQmMHj2aHj16EBMTQ9++fXnqqacuuD0hhBBCCCGEEKI2cjcZUWfS0tJYuHAhffv2JSgo6LyPLy4upkWLFtx+++34+/tz8uRJ3n77bfbt28fy5ctxcnK6BFELIc5FeXk5y5YtIyEhgdzc3HM+zmAwEBMTw/Dhw+XuN0IIcZW60DHgVKqqYrPZsFqt2Gw2VFVFVVX7++7u7kRERDB48GCioqJwdnauq/CFEKJWkgwRdUJVVSorKy+qjfj4eOLj4+2v4+LiaNKkCaNHjyYhIYHOnTtfbJhCiPOgqir5+fkcO3aMt99+m7y8PIf358yZA8C0adNO20ZBQQFHjhxh/fr13HXXXQQHB+Pj44Ovry/u7u4oinJJ+yCEEOLCqKpKaWkpGRkZvPrqq3U6U7e28aOyspL8/Hx27dqFotHQqmVLYmJi6NChA23btpXkiBCizkkyRFyQJ598koSEBKZOncprr73G0aNHMZvNAAwbNsxe79ChQwAkJSUxY8YM9u7dS1BQEOPHj+fHH3+krKyMTz755LTn8fHxAcBisVy6zgjRgNlsNsxmMxaLBbPZjNlsprKyEpPJhMlkorS0lNLSUoqLiykoKCA/P5/c3Fxyc3PJzs62/399sQoKCnjrrbccynR6PX6+vvj5+eHn54e/vz/+/v4YDAb7az8/vzo5vxBCCEeqqlJeXs7Ro0cpLCwkMzOTzMxMsrKyyMnJITc3t87GgPOOTedCUnIySUlJrFixAp1OR2RkJG3btiUyMpJWrVrh6+srCXUhxEWRZIi4YNnZ2bz00ks8/PDDeHl5kZqayqxZs5g9ezYtWrSw16uoqGD06NF4eXnxyiuvALBgwQKKioqIiIio0a7VasVqtZKSksIrr7xCu3btZFaIaBSysrJYs2YNJpOpxnuKotinFFcnMkwmExUVFQ6vTSaTPdlRPRX5gigKKFrQOYHFDKhnPeTsbWpA7wyqCjYbFquN7JyqpMuZuLm74+bqisFgwMfHB3d39xpLbtzd3enfv78kT4QQAjhx4gRr1651GAMsFgslJSUOie/axhvgnzFA6wTWOhoDzoeTK9pud6AWZaHmp2PJT+dAYiIHDhywV3F3dyckJISAgAACAgK48cYbL2+MQoirniRDxAUrLCxkyZIldOjQAYAtW7YA0Lp1a9q3b2+vt3z5cnJzc/nss89o2rQpAG3btqV///61JkPuvvtudu7cCUBMTAzvvvsuOp38VRUN3+uvv05iYmJ9h1FFVUG1wAXmUmpv0waV5ad/3ysITWAL1KJs1KIcqCgCoKy0lLLS0rOuU8/OzmbixIl1GLAQQlyd5s6dS1pa2rlV1jmh+Iai+IZiyzoChZn/jAH1SNHqUHxDwTcUANVSiZp3ElvaASjKprS0lKSkJJKSkoCq5EjLli3rM2QhxFVG/oUpLpivr689EXImCQkJREVF2RMhAOHh4bRu3brW+i+99BLFxcWcOHGCd999l/vvv5/PP/8cDw+POotdiCvR3XffzbvvvktBQYG9TFXVGtOAT91wTlXBbK6aCXJq+UVRNKDR/vPTav77yuBF0jlXzTRRbVUPm+2f56oKRVnYik6/Jt3Nzc0hMVo9W0ZRFIKDg7nlllsuPkYhhGgA7r33Xj788EPKysr+NWZUbWJaWlr6T2VLJWrOMdScY46//xUNaDRgtdTNGHAeFP/wqngry1DzM1Dz01ALM6G8yKGeXq/H2dmZJk2a0LFjR4qLiy9rnEKIq5skQ8QF8/f3P6d62dnZtU5d9/f3r3UvkOolNrGxscTHx/Of//yHZcuW8cADD1xcwEJc4dq1a8cbb7xxQceqqmrfD6SwsJDExESaNWuGVqu17xdS/bN6GU1FRQXl5eWUlJRQVFREYWEhRqOR3Lw8Ck9JyNQZi6nq8S8urq74+/nZ9wr5954hLi4upKenExMTg5ubW93HJYQQDUxcXBxxcXGnfd9sNpOWlsaOHTtwc3MjPz+frKws+74hhYWFlzHaf3FyQ60owbL1aygrsBd7eHjQNi6OyMhIWrZsSdOmTfH397dfMCgrK7tyZlcKIa4KkgwRF+xcN60KDAysdXDKy8vD29v7jMcGBAQQGBjIiRMnLihGIRoLRVFwcnLCyckJjUaDr68voaGhF5w8MJvN5OTkkJ2dTVZWFmlpaezcuZO8vDz7LJQz3UVGURR7TL6+vkRHR+Pv74+3t7f9bjLVj7PdcresrKxO72IghBCNnV6vJzAwkIiICKKjo2uMFSaTCaPRiNFopLi4mPz8fDZt2kR6ejolJSXA6b8HVu9vZbPZ7I9/z1w80/hBZRlkJeHi4kLbzp3p0KEDHTp0ICIiAo1Gc3EdF0KIU0gyRNQZvV4PUGMzrpiYGFauXElKSop9qczJkydJSkqiS5cuZ2wzLS2NzMxMhyU2QohLT6/XExISQkhIiL1s9OjRNepZrVb7DC+NRoNOp5Pd/YUQ4ipXvfSkSZMm9rKbbrrpgtsrLy8nNTWV1NRUMjIyyMnJIT8/n/LycqxWK3q9Hm9vbwICAggPD6dly5b22Y1CCHGpSDJE1JmIiAi0Wi3Lly9Hq9Wi0+lo3749Q4cOZdGiRYwdO5ZHH30UVVVZsGABBoPB4R9Nzz77LL6+vrRv3x4PDw+OHTvGBx98gMFgcLhdrxDiyqHVauXLqhBCiDNydXWldevWp90vTggh6oMkQ0Sd8fPz49lnn2XJkiWsWrUKi8XCoUOHcHFx4YMPPuC5555jypQpBAUF8cgjj7B8+XI8PT3tx3fo0IEvv/ySzz77jMrKSpo0aUKfPn0YO3Ysvr6+9dgzIYQQQgghhBANiSRDxAV5+eWXay0fMWIEI0aMqFHeunVrPvvsM/vr/Px8XnrpJe6//3572bBhw2QGiBBCCCGEEEKIS06SIeKyePfddzEYDISGhpKTk8MHH3yAzWZj6NCh9R2aEEIIIYQQQohGRpIh4rLQarUsWrSIzMxMtFotsbGxLF261GFjLiGEEEIIIYQQ4nKQZIi4LB544AEeeOCB+g5DCCGEEEIIIYRAbtYthBBCCCGEEEKIRkVmhohL4p577sHNzY3Fixef13EffPABq1atIjU1FYvFQtOmTRk+fDh33XWXw214hRBCCCGEEEKICyXJEHFFKS4u5uabb6Z169bo9Xo2bdrEiy++SElJCWPHjq3v8IRoVMrLy1m2bBkJCQnk5uZedHsGg4GYmBiGDx+Oq6trHUQohBDicqvrseF0ZMwQQlxqkgwRV5SJEyc6vL722mtJT09nxYoVkgwR4hKzWCwcP36c5ORkkpKS+PPPP6moqKi17pw5cwCYNm3aObdfUFDAkSNH+OOPP7j//vsJDQ0lMDAQDw+POolfCCFE3SsrKyMtLY3MzExSUlL44YcfKCkpOeMxFzJG/Fv1mLFp0yamTJlCcHAwnp6eMlNYCFFnJBkiLsgXX3zB4sWLMRqNdOzYkcmTJ3PHHXcwe/ZshgwZUqP+ggUL+OCDD/j888+ZOXMmBw4coGnTpkybNo1evXqd8Vy+vr6YzeZL1RUhGiVVVcnLy+PIkSMcPnyYgwcPkpSUdFn+XzMajbz22mv2156eXoSHN6VZs2ZEREQQERFBeHg4Li4ulzwWIYQQVcrLy0lLSyMlJYUTJ05w8uRJTpw4QV5eXr3GlZWVxRNPPAGATqfDz9+foMBAgoODadKkif3h5eVVr3EKIa4+kgwR523t2rU899xz3H777fTv35/ExESmTJly1uPMZjNTp05l1KhRjBs3jnfffZdHH32U3377DV9fX4e6FosFk8nEtm3bWLlyJePHj79U3RGiQTt+/DiZmZkUFRWRn59PTk4OGRkZnDyZQnFx0T8VFQXc/cDLA8oKoKIEVOulC0xRQOcMNivFpaXs37+f/fv3n/K2QkBAACEhIfj7+2O1WsnMzMTf3x9XV1datmwpM0qEEOIcmc1mEhMTKSkpoaysjKKiIvLy8jh+/Dhms5mcnBzy8/NrHujsgeIXhqrRQanx77HBdvk7gAJaPRZVJdtYSHZWFvv27atRy93dneDgYAICAvDz88Pb2xtPT088PDxwdXXFzc2NyMhI9Hp9PfRBCHGlkWSIOG/vvPMO3bt358UXXwSgV69emEwmFi5ceMbjzGYzjz/+OH369AEgPDycG2+8kT/++IPbbrvNXu/EiRPceOON9tcPP/ww9913X913RIgGLiMj48yJSnc/NIEtwCsQxSsARavHuu9n1PLCSx+cqoL5lCU4fk3RNr8GtcSIWpSFmn2U7OxssrOz7VXWr19vfx4dHc3s2bMvfZxCCNEArFy5kk8//fTMlfQuKP7hKF6BKO6+4O6LonMC+HtsKDrz8ZeUCtZKABSfYDTtroeK4qqYyopQi3NQ89MoLS0lOTmZ5OTk07Y0bNgw7r777ssVuBDiCibJEHFerFYriYmJ9umK1fr163fWZIhGoyE+Pt7+ulmzZuj1erKyshzqNWnShK+//pqysjK2bdvGe++9h0aj4dFHH627jgjRCPj6+hITE0NCQkLtFUqN2FJKUDwMqN6BKF5BKMFtUC1mKCsEc/mlC07vCk6u9iuMit4F24ndqCVGqDj7F+6ePXteutiEEKKBadu2LU5OTlRWVp6+krkCNfMwan7aP8kQdz8Ud1+UkOjLMzacjt6l6qFowM0X24nd/yRDyosck+tnoNVqiY2NvbSxCiGuGpIMEefFaDRisVjw8/NzKPf39z/rsS4uLjg5OTmU6fV6TCaTQ5mTkxPt27cHoFu3bri5ufHqq68ycuRIAgICLrIHQjQeLi4u9hlcULX8LC8vj4yMDFJSUjh69ChHjhwhNTUVtSAdtbqiqxd4GqA455y/YJ4XRQM2S9WU67+pZQUAeHt7ExEVS7NmzWjatCkhISF4eXmRmZlJu3btcHNzq/t4hBCigWvXrh1ffvml/bWqquTm5rJjxw68vb3Jz88nIyODtLQ0Tpw8SV5uKhhT/xkXAJzcwNm96ve39TLv5WausI9H6iljh06ns+8dYjAYsNlsxMTEEBoaip+fH56enmi12ssbqxDiqiHJEHFe/Pz80Ol0GI1Gh/JLublWu3btsFqtpKWlSTJEiIug0+kICgoiKCiIjh072stLS0vtm6gePnyY5ORkiowpZ2zrjHcIUBSq9/pXVfWUYgWdToeXlxfe3t74+voSGBhISEgITZtWbaD67/2DoOpOBqculxFCCHFxFEXB3d2dgIAAoqOjaySaS0tLOXnyJCdPniQlJYW0tDTS09PJzc3Faj37flLTpk1Do9Gg1WpRFMX+qI2qqvax4tQxQ6fT4ebmRmhoKJ6ennh7e+Pj40NAQAABAQEEBwfj7+9vT3aUlZWRmJhYa3+EEKI2kgwR50Wr1RIdHc3atWu599577eW//vrrJTvnjh07UBSFsLCwS3YOIRozd3d3OnXqRKdOnYCqL6MFBQX2L76FhYWYTCZsNhs6nQ5nZ2fc3d3x8vKyP6o3p9NoNPXcGyGEEBfL3d2d6OhooqOjHcptNhvFxcUUFxdTUVGBxWIBcBgbPDw8aswEFkKIK5EkQ8R5e/jhhxk3bhxPP/00AwYM4MCBA3z77bcAF/UPoeLiYh588EFuvfVWmjVrhsViYfPmzXzyyScMHz4cg8FQV10QQpyBoij4+vrWOktDCCFE46XRaPD29sbb27u+QxFCiIsmyRBx3vr168eMGTNYvHgxq1atIjY2lueee44HH3zwom516ezsTPPmzfnoo4/IysrCxcWF8PBwZs6cyaBBg+quA0IIIYQQQgghGjVJhogLMnLkSEaOHGl//dVXXwHQpk0bAD755BOH+hMmTGDChAk12tm1a5f9uZOTk9wqUwghhBBCCCHEJSfJEHHeCgoKWLhwId27d8fd3Z19+/axaNEi+vXrJ/t6CCGEEEIIIYS44kkyRJw3nU5HSkoKq1evpqioCF9fX2677TYef/zx+g5NCCGEEEIIIYQ4K0mGiPPm4eHB4sWL6zsMIcTfKioqWLNmDbt378ZqtRIZGUl4eHh9hyWEEOIKkZKSwq+//kpSUhJms5mwsDDi4uLQ6/X1HZoQQtQbSYYIIcRV7PDhw7zyyivk5OTYy/bs2YOrqysPP/wwvXv3rsfohBBC1KeioiI+/PBD1q1bV1WgaEDRkJSUxLp16wgLC2PixIm0bt26fgMVQoh6IMkQUW8qKyt57rnnWLduHfn5+UyfPp0uXbowb948Dh06RGFhIQaDgWuvvZaJEycSFBRU3yELcUXZvHkzr772GhaLBaVZRzRhMaBoUDMOU35sO/PmzSMrK4thw4ahKEp9hyuEEOIy2rVrF6+//jqFhYXgYUAT0QnFL6wqIVKSh+3kHlJTj/HUU08xZswYbrjhhvoOWQghLitJhoh688033/Dtt9/y8ssvEx4eTmhoKEeOHKFFixbcfvvt+Pv7c/LkSd5++2327dvH8uXLcXJyqu+whbgi/PrrryxcuLDqS62LF2r6Iazph/6p4OIJphI+/fRTMjIyePjhh2U6tBBCNAJms5lPP/2UlStXVhU4u4OpFNuhP/+p5OKO4tMEJfo/WI5s4q233uLgwYOMGTMGZ2fneolbCCEuN0mGiHqTnJxMYGAgt956q70sICCA+Ph4++u4uDiaNGnC6NGjSUhIoHPnzvURqhBXDKvVymeffcby5csBBVQblBcCMGfOHACmTZsG5vKqAzRafvvtN06cPMljEyfStGnTeopcCCHEpZaUlMTChW9x4sTxqmS5agNTKVBzjFCLc8HNB03sTaiH/mTt2rUkJSUxadIkmjdvXo+9EEKIy0OSIeKCffHFFyxevBij0UjHjh2ZPHkyd9xxB7NnzwZg+vTptR63du1aRo0aRVpaGgBRUVH28tpuzevj4wOAxWK5BL0Q4upgsVjYvXs3n3/xBclHjoDOGSymsx9os4KHP8lHjjBx4kSuvfZa4uPjadGiBQaDQWaLCCFEA3D06FFWrFjBhg0bqgo8DFCSe/YDywpQc46j7XQztuStnDy5nylTpnDTTTcxdOhQ/Pz8Lm3gQghRjyQZIi7I2rVree6557j99tvp378/iYmJTJkyxf5+3759WbZsmcMxs2bNIiUlBW9vbxYuXMiiRYvYuXNn1VR/IDAw0F7XarVitVpJSUnhlVdeoV27djIrRDQYNpuNwsJCCgsLyc3NJTs7m8zMTLKzsyksLKSsrAybzYZWq0Wj0WAymcjJyaGyshIAJbh11RW9c0mG/E3Tvj+2o9v4888/+fPPf6ZKu7m74+fri8FgICgoiCZNmhAcHIzBYMDHxwcfHx90OhkqhBDiYpWWllJaWkpxcTGFhYXk5+eTn59PQUEBRUVFlJWVYTab0Wg0ODk54eHhgZeXF97e3nh7e+Ph4YGLiwtQdRcxo9HIiRMn2LNnj/0CE54GtC27Yz34xznHpWYnozS/Bm3reGz+4ahHNrJ69Wp+/OknOnXsSGxsLC1btqRt27ay/5QQokGRb7jigrzzzjt0796dF198EYBevXphMpnsiQ0/Pz+Hqwnvv/8++/fv54MPPsDT05O2bdtiMBhwcnKiY8eONdq/++672blzJwAxMTG8++678g8y0WC8/PLLbN269fQVFAUUbdWsDlTQ6sDNByUwGE1wJIqHH5btK87rnBr/plUb55Xkoeano5YVgKmUsspyytIzSE1NrfU4f39/Fi9eLP//CSHERfj000/56quvLln7SkBzlCZRKL6hVQmL6qWS56Lyn7oav1DULkNRs45gS9vPjh072LFjBwCPPPKIbLIqhGhQNPUdgLj6WK1WEhMTue666xzK+/XrV2v9P/74g9dee40nn3ySbt26ndM5XnrpJb788kteeeUVTCYT999/PyUlJRcduxBXAoPBcOYKqgo2C6BWvbZaoLwIygtRy4tQVRXFP/ycz2eva7NWHX/Kg4qiv5MutQsICECjkaFCCCEuhq+v7yVtXy0vqhonqn+fG5qd+8H/rmupRC0vRPl7r5Fq7u7uFxmlEEJcWeRSnzhvRqMRi8VSYx2pv79/jbrHjh1jypQp3Hbbbdxzzz3nfI4WLVoAEBsbS3x8PP/5z39YtmwZDzzwwMUFL8QVYMyYMYwZMwaTyeSwTCYnJ4f8/HzKy8ux2WxoNBq0Wi0mk4nMzExSU1NQ81LAOxhNy+6oOcegrODMJ3PzQQltiy11P+qJXajmCgC0Wi2+vr74+AThe8oymeDgYIKDgwkICMDNzU2mRAshRB0YOHAgAwcORFVVKioqKCgosC+Tyc/Pp7i4mPLycsxmM4qi4OTkhLu7O97e3vj4+ODl5YWnpydOTk4oikJFRQV5eXmcOHGCvXv3snv3bixJG1FO7EZp0QVNy27YinPPaYzQtuoOgKqqqBkHsR3dBpZKvLy96d3revsyGdk/RAjR0EgyRJw3Pz8/dDodRqPRoTwvL8/hdXFxMePGjSMiIoKZM2de8PkCAgIIDAzkxIkTF9yGEFciZ2dnQkNDCQ0NPaf6aWlpfPrpp2zcuBF1349o2vwHtSANtSADKkqZ9vSMqop6139umxgSjXrgN9SCDNzd3Rk46Hbi4+MJDw+XpS9CCHGZKYqCq6srrq6uNGnS5KLaCgsLIzY2lltvvZWioiK+//57vv32W0wH/0DxDUUTcz1qxqEzjhGaZp1QdE6olRXYDv6OakzBzd2dO+8bxY033oiTk9PFd1oIIa5Q8k1YnDetVkt0dDRr167l3nvvtZf/+uuv9uc2m40pU6ZQXFzMRx99dFGDaVpaGpmZmXJLUNHohYaG8sQTT7B27VrefvttrPt/QdP2OrQta19+ppbmY9vzI2pFMd27d2fcuHF4eXld5qiFEEJcal5eXtx5553079+fRYsWsW3bNtj1PUpUr9OOEdXUohxsB9aiVpTQuXNnJkyYcMmX9QghxJVAkiHigjz88MOMGzeOp59+mgEDBnDgwAG+/fZbADQaDe+99x6///4706ZNIyMjg4yMDPuxbdu2PW1y5Nlnn8XX15f27dvj4eHBsWPH+OCDDzAYDAwbNuyy9E2IK12/fv0wGAzMmjUL0/5foXUPNCFtHOrYjKlVM0Isldx1110MGzZMlrwIIUQD5+/vz1NPPcWaNWt4//33qUz4BTUkGk2Lrig6x+9eqmpDTd2P7eg2FFTuueceBg8eLPtECSEaDUmGiAvSr18/ZsyYweLFi1m1ahWxsbE899xzPPjgg3h4eLB582YA5syZU+PYtWvXEhYWVmu7HTp04Msvv+Szzz6jsrKSJk2a0KdPH8aOHStXKYQ4RWxsLC+88AIvvvgiRYf/RC1IRxMWAxottvRDqOkH0Ov1PDplCr169arvcIUQQlwmiqLQv39/oqOjmTdvHsePJ2LLPYHStH3VhtpaHWphJraT+6AkF29vb6ZMmUKHDh3qO3QhhLisJBkiLtjIkSMZOXKk/XX1LePatGnD9ddfz8svv3zG45999lmeffZZh7Jhw4bJDBAhzlFkZCSvvPIK8+fP5+DBg1izj9rf8zcYmDxpEu3atavHCIUQQtSX8PBwXnnlFVasWME333xDRfIWSN5if19RFNp36MC4ceMIDg6ux0iFEKJ+SDJEXJCCggIWLlxI9+7dcXd3Z9++fSxatIh+/fqddtaHEKLuBQUFMWvWLHbs2MGuXbuw2Wy0bt0aT09PmjdvXt/hCSGEqEd6vZ477riDgQMH8tdff3H48GHMZjNhYWFcc801FBQUyF5SQohGS5Ih4oLodDpSUlJYvXo1RUVF+Pr6ctttt/H444/Xd2hCNDoajYauXbvStWtXAMrKykhMTKznqIQQQlwpPDw86N+/P/3797eXlZWVUVBQUH9BCSFEPZNkiLggHh4eLF68uL7DEEIIIYQQQgghzptsFy2EEEIIIYQQQohGRZIhQgghhBBCCCGEaFRkmYy4YmVmZnLTTTdRVlbGpk2b8PPzq++QhBBCCCGEqDeqqpKdnc2xY8c4efIkWVlZFBQUYDKZUBQFd3d3DAYDTZs2pVWrVkRERKDVaus7bCGuSJIMEVesl19+GTc3N8rKyuo7FCGEoLy8nGXLlpGQkEBubu4Ft6OqKhaLBZ1Oh6IoGAwGYmJiGD58OK6urnUYsRBCiKuVyWTis88+IzExkaysLCwWC1arFYvFgqqq59yOm5sbHTp0oEuXLnTp0gUfH59LF7QQVxlJhogr0qZNm9i0aRMPPfQQc+bMqe9whBCNXFFREf/73/9ISUm54Daqf5dNmzbNobygoIAjR46wYcMGRo0ahY+PD05OTjg7O+Pi4oKLiwuurq44Ozuj0cjqViGEaGhsNhslJSUYjUays7M5fPgw3333HSaT6ZyOr3V8cXIDv1DKCrPYvHkzmzdvRlEUWrduTdeuXenYsSMtWrSQWSOiUZNkiDgnX3zxBYsXL8ZoNNKxY0cmT57MHXfcwezZsxkyZAgAK1eu5KOPPiI5OdmehZ4xYwahoaF88803TJ8+nW+++Yb58+ezfft2AgMDGTduHIMGDXI4l9ls5oUXXmDChAm4ubnVQ2+FEJeTzWazX+0ym801fpaUlJCamgpU3da7+uqYzWazP6xWK6qqoqoqNpsNoMbrf9evPmf1eSorK+0Pk8lERUWF/ZGbm0tpaekl/Rzy8vKYP3/+GetUJ0bc3Nxwc3PDxcXF/tzV1dXhUV3XxcXFnlipTrJU/9TpdPYZKkIIIc7MZrNRXl5OWVkZpaWllJSUUFJSQnFxMaWlpfZHeXk55eXl9jGlerypHruqX5tMJipMJsrLys5rtsc5qSxDcfZA260PankRat5J1NyTHE46wuHDh/n0009xdXUlMjKSli1b0rRpU5o0aYLBYMDb2xu9Xl+38QhxBZJkiDirtWvX8txzz3H77bfTv39/EhMTmTJlikOdJUuW8MorrzBs2DAmTZqE2Wxm8+bNGI1GQkND7fWmTp3KHXfcwf3338+yZct48skniYmJoVWrVvY6H3/8MVqtlpEjR/Ltt99etn4KIerO559/zrJly+o7jIun0YJWD5bKy3M+RQN6F0AF9e8H//ysqDRTYaokv6Dg77LLZ/Dgwdx7772X9ZxCCFFffv/997MmqM+ZogGNpuqnqoLNesp7StVYwyl1zBWg2i76tGrKXiwFGWgiOqMJi4GwGFRLJWp+Gmp+GuUFmezZs4c9e/bUevxjjz1G3759LzoOIa5UkgwRZ/XOO+/QvXt3XnzxRQB69eqFyWRi4cKFABQXF7Nw4UKGDx/O888/bz/u+uuvr9HWXXfdxV133QVAbGws69evZ82aNfZkSFZWFm+99RZvvfWWTNsT4ip2MctJ6p8CeueqpITOCUWrRy3KAetlSIioNqi8MvdJOnHiRH2HIIQQl01mZubFNaBzAp0L6PRVyQ5Fg6IoqKX5YDX/U+/UvLa1RisXx2aFwkzU1H3gGwKAonMCDwOYTWC1oFpMUFle6+E5OTl1HJAQVxZJhogzslqtJCYm8sQTTziU9+vXz54M2bVrF+Xl5QwbNuys7fXs2dP+3MPDgyZNmjgMNnPnzqVHjx7Ex8fXUQ+EEPVh6tSpmM1m+3Tgf/+sXp7y76Uqtf0sKysjLS0Ng8GARqOxH3O6JTLVz099nFpevUTm3+euXipjMpn+WSZTXorFbD57h+uJotGg1Wjw8/PD19fXvoSmennMqQ9nZ2eHJTJ6vR4nJyf0ej06nQ69Xo9Wq0Wn06HVatFqtWg0GjQajb1cCCEai+HDh3Prrbc6LKGsXv5y6pKY4uJi+3KZU5fJVFRUYDKZqKwsw2Ky2Mery0rvCm7eKKExqAWZ2PJOoOadhLJCexUPT09aRsfWWCbj7e1NcHDw5Y1XiMtMvtmIMzIajVgslhq3tfX397c/LygoACAwMPCs7Xl6ejq81uv1VFZWXW3dtWsXP//8M19++SVFRUVA1d0bAEpLS+3r4IUQVz5FUXBycqqTtsrKykhMTCQ6Orpe9hGyWq1kZ2fz/PPPk5GRccnO4+npSb9+/fDy8rInLKo3Tv33fiDV5bLXhxBCXDp1/d2zOjlfnYyvrKykoqKCsrIyCgsL7RuoHj16lJ07d15c8sTFEyW0LRTnYEv8rWomCFV7T3Xs3p2OHTvSvn17QkJCZCwRjZYkQ8QZ+fn5odPpMBqNDuV5eXn259W36MrOzr6oDPKxY8cwm80MHjy4xnvXX389AwcOrLu1m0IIcY60Wi1NmjRh3rx5F3Vr3dmzZwPg7e0tt9YVQohGSFEU+8w7Jyen0yb4y8rK2L17NwkJCezdu5e8vDz7TMbaNlqdNu1JUKjafwSlasllRTFq8hYAAgIC6NKlC3FxccTExMjmqEL8TZIh4oy0Wi3R0dGsXbvWYeO8X3/91f68U6dOuLq6snz5cjp06HDB5+rVqxcff/yxQ9mGDRt47733eOutt4iIiLjgtoUQ4mK5urpy3333XXQ79T3TRQghxJXP2dmZu+++u8Y4kZeXx+HDhzl69CgpKSlkZmZSUFBAhcmERqPB3c0Ng8FAWFgYrVu3pl27djL7Q4jTkGSIOKuHH36YcePG8fTTTzNgwAAOHDhgv8uLRqPB09OTRx55hFdffRWbzcb111+PzWZjy5Yt/Pe//6V9+/bndJ6AgAACAgIcytLS0gDo3LlzjaU6QgghhBBCNCb+/v7Ex8fL/npC1AFJhoiz6tevHzNmzGDx4sWsWrWK2NhYnnvuOR588EE8PDwAePDBB/Hz8+Ojjz5ixYoVuLu706lTJ4e9RYQQQgghhBBCiCuBJEPEORk5ciQjR460v/7qq68AaNOmjb1s6NChDB06tNbjhwwZwpAhQ2qUf//992c87+mOE0IIIYQQQgghLpQkQ8RZFRQUsHDhQrp37467uzv79u1j0aJF9OvXj7CwsPoOTwghhBBCCCGEOC+SDBFnpdPpSElJYfXq1RQVFeHr68ttt93G448/Xt+hCSHOwfHjxzlw4AAWi4VWrVoRHR0tG6kJIYQQQohGTZIh4qw8PDxYvHhxfYchhDhPmZmZfPzxx+zevduhvHnz5owdO5aoqKj6CUwIIYQQQoh6JskQIYRogPbv38/3q1dTaTKh+IahBLcCjRY19wTHjh1h+vTp3Hvvvdx6660yS0QIIYQQQjQ6kgwRZ2U0GomPj2f27NnnvJnpN998w/Tp09m0aZP9lrhGo5HXX3+dDRs2YDQaCQ0NZfjw4YwaNcrhH2MlJSXMnTuXn3/+mcrKSrp168YzzzxDaGjoJemfEA2JzWbjyy+/ZOXKlSg6JzRtr0MT2OKfCgHNUUPaYDuwjg8//JC0tDQeeughtFpt/QUthBDislBVlfT0dJKSkjh27BjHjh3jp59+QqvV4urqire3N4GBgYSGhhIeHo6Pj48kzIUQDZYkQ8RlM2HCBE6cOMGkSZMICQlh48aNzJo1C5vNxv3332+vN2XKFPbv388zzzyDh4cHb775Jvfffz+rVq3CxcWlHnsgxJWtpKSEN998k61bt4LOCdXJHTVpE7akTf9UcnFH8WmCEjsQEtexZs0acnJymDJliv1W2UIIIRoOk8nEnj172Lp1K9t37KAgP/+cj9XrnYiMbE3Lli1p3rw5ERERhIWFodfrL2HEQghxeUgyRFwWmZmZbN++nVmzZtlvvxsfH8/Bgwf54Ycf7MmQPXv2sH79et5991369OkDQGRkJDfccAMrVqxwuL2vEKKKzWZj8+bNLFmyBKPRCBodWCqrHsCcOXMAmDZtGpjLUYtzUfNS0HQYAEmb2LVrF1Mef5zJkybJPiJCCHGVU1WVzMxM9u7dy44dO9i9Zw+VJlPVm/+a5eEwPtTCbK5k//797N+/316m1WoJDw+nRYsWtGjRgpYtWxIRESEXrIQQVx1JhogavvzySxYtWkReXh4dO3Zk0qRJDu+vXLmSZcuWkZycjKqqtGnThqlTp9KhQ4fTtmk2mwHw9PR0KPfy8iL/lCsUv//+O15eXvTu3dteFhISQufOnfn9998lGSIaBbPZTGZmJunp6eTk5FBcXIzFYsHJyQkPDw88PDxwdnamoqKCkydPsnnLFtLT0kCjAZ8QKEg/+0nKClAzDqGJ6Yft+C6yTuziySefpGfPngwYMIA2bdrI0hkhhKgnNpuNsrIy+6OiogKz2YzZbMZisWC1WrFarZjNZsrLyykuLsZoNJKRkcGxY8cpLi76pzE3X5TwaKgsQ808fEHxKMGRKJ4BqKVGrCV5HDuZwrFjx1i7dm3V+4pCaGgozZs3p1mzZoSFhRESEkJAQACurq518ZEIIUSdk2SIcLBu3TqeeeYZhgwZwsCBA0lISGDy5MkOdVJTUxk0aBDh4eFUVlby/fffc9ddd7Fq1SqaN29ea7tNmzalR48eLFq0iIiICEJCQti0aRO//PILzz//vL1ecnIyzZs3r7E+tVWrVvz5559132EhrhAfffQRK1euvLCDNTqU4Eg0zTpi3fPTOR+mpuxD9WmCtvk1qH5hWJM2sWHDBjZs2ADA448/Ts+ePS8sJiGEEGf0xx9/MG/evLpv2NULJbBl1ZJI31DUimJsx3dCcc4FN6lmJ6OW5J1yDm+wWQEV3HzBYiI1M5vU1FT7GFKb1q1bM336dPt+ckIIUZ8kGSIcvPPOO3Tp0oXZs2cD0KtXL8rLyx1urTt+/Hj7c5vNRo8ePdi3bx8rVqyokTg51cKFC5k0aRK33HILUHUVYerUqQwaNMhep6ioqMbsEaiaQVJYWHix3RPiimS1WvnppzMkMbROoNWBxfT3l89TaLRowjugNIlCcXYHc/m5n9hmQU3dB74hKN5BaFp2xbZvjf0cBw8elGSIEEJcItWzKuqEogFPQ1UCxDMAxSugakwAbEc2QmHmxbVvs8KpyZBTuXiiaRGHWpKHmncSNe/kaZtJSkri+PHjkgwRQlwRJBki7KxWK/v372fq1KkO5f3793dIhiQnJzNv3jx27dpFXt4/A+Px48dP27aqqkyfPp3jx4/z6quvEhQUxNatW5k/fz5eXl7cfvvt9rqn27VcdjMXDZVWq+WNN97g6NGjGI1GsrKySE9Pty+TMZsrwVq1/0f1UhlnZ2fKy8spKCiouuJ3YjdKSDT4NYWco+d2Ymd3lLD2qJUV2A7/iZp7HICoqCg6duxI//79L1GPhRBCPPnkkxw8eBCo2uS0oqKCsrIySktL7Y+ysjLKy8upqKigsrISs9l8yhIZS9UymYryqj1BirJRi7JRq0/g4oniFwZeQVV7SJUVnV/C/FR6V3B2A1WtSozYLGC1VD03pmE1ptqrajQagoKCCAkJITAwED8/P7y9vfHw8MDPz0/2phJCXDEkGSLsjEYjFoulRrbeYDDYn5eUlDB69Gj8/Px48sknCQkJwdnZmaeffhpT9eZctVi/fj0//fQT3377LW3atAEgLi6OoqIi5s6dy9ChQ9FoNHh5eZGRkVHj+KKiIry8vOqop0JceYKCgggKCqpRrqoqJpMJi8WCs7NzjR38i4qK2LJlCytWrCA9bT84u4OLJ1QUn/mEbj5oO90MFSXYdqxANZUSExPD6NGjadGixZmPFUIIcdFcXV3p1KlTnbRlNpvJy8sjIyOD48ePc+jQIRISEihJT6yqoNWDTxMozoXKsvNrXO8Cnv5QVlhjbHFxcaF589b2zVSbN29O06ZN5W4zQoirgiRDhJ2fnx86na7qbhSnyM3NtT/fvXs3mZmZLF682J7UACguLiY4OPi0bR85cgStVlvjakB0dDRLly6loKAAPz8/WrZsycaNG1FV1WEmyJEjR2jZsuXFdlGIq46iKGfcod/Ly4sbbriBvn378s0337Bs2TJsNhV8w8BSARWlTHt6RlVlvav91rqaZp1QCzKwHViHoloZNWoUgwYNQqPRXJ6OCSGEqDN6vZ7g4GCCg4PtCRar1UpSUhJbt279e6PtU5avKFW/66dNfwpQqu5Chlo188P+82/mCjCm4u3jQ4voTkRERNgTHyEhITJuCCGuWpIMEXZarZa2bdvyyy+/cN9999nLf/75Z/vziooKAIeM/86dO0lLS6N169anbTs0NBSr1UpiYiJt27a1lyckJODm5oavry8Affr04a233mLDhg32O8pkZGSwc+dOnn766TrppxANkV6vZ/jw4bRt25aX58yhND8VJSwGTac4lH99UVVVFTU1AVvyFpxdXHhi6nSuueaaeopcCCHEpaDVamnTpg1t2rRh1KhRpKWlsXPnThISEjiclES+0QhqzeNcXFwICQmhadOmREREEBERQfPmzfHx8bnsfRBCiEtJkiHCwdixYxk3bhzTp0+3303m+++/t7/fsWNH3NzcmDlzJmPGjCErK4uFCxfWOr3/VH369CE0NJSJEyfyyCOPEBQUxObNm/n8888ZPXq0fRZIbGwsffv25X//+x9PPvkkHh4evPHGG4SGhjJ48OBL2nchGoL27dvz4gsvMGvWLHJSE7AWZaON7IHi4Q+AWlGC7cgm1NwT+Pn58fTTT8uyGCGEaARCQ0MJDQ21b2Sfm5vLtm3baNKkCa6urri4uODr64unp6fs0yaEaBQkGSIc9OvXj5kzZ7Jo0SJWr15NbGwsr732GiNGjACq9g954403mDt3LuPGjSMiIoIZM2awZMmSM7br7u7O0qVLmT9/PvPmzaOwsJCwsDAmT57Mvffe61D3tddeY86cOcycOROz2Uy3bt1YsGDBGZcKCCH+ERQUxOjRo9m4cSMbNmzAun0FuPuBRgsluaCqtG/fnkmTJsmO/kII0Ui5ubkRGBhIZGQkbm5u9R2OEEJcdpIMETWMGDHCnvyodujQIfvz3r1725ewVOvTp4/D6yFDhjBkyBCHsqZNmzJv3ryznt/Dw4MXXniBF1544XxDF0L8zcnJiYcffpjrr7+eb7/9loT9+7FaLLSKjGTgwIH06tVL1nkLIYQQQohGS5IhQgjRgMXGxhIbGwtQY2NiIYQQQgghGiu5LCiEEI2EJEKEEEIIIYSoIskQIYQQQgghhBBCNCqSDBFCCCGEEEIIIUSjIskQUad+/fVXPv300ws+/q+//mLKlClcf/31REVF8fzzz9dhdEI0PGazGaPRSHZ2Nvn5+ZjN5voOSQghhBBCiCuebKAq6tSvv/5KQkICd9111wUd/8cff5CYmEjXrl0pLCys4+iEuLqpqsqxY8fYtm0bv/32G7m5uVit1hr1FEVBURR0Oh06nQ6tVotWq7W/bzAYiImJYfjw4bi6ul7OLgghhKgHJSUlHDx4kAMHDrBp0yby8vIwm82oqmqvo9Fo7I/qcUOr1Z5xvykZT4QQVzNJhogryrRp05g+fToAW7ZsqedohKh/VquVgwcPsmnTJjZt3kxebu4Z68+ZMweo+n+psrKSysrKGnUKCgo4cuQIv/32GzfccANubm7o9XpcXV3x8vLC39+fJk2a4O7ufkn6JIQQ4tIymUwcOHCAPXv2sHfvXo4dO+aQ+ADH8QLAZrNhs9nO6zzV48mOHTuYNWsWnp6eddMBIYS4DCQZIs5bUlISc+fOZe/evVRUVBAcHMywYcNITk5mxYoVAERFRQEwePBgXn75ZZ588kkSEhKYOnUqr7zyCidOnKB169Y8++yzdOzY0d62RiMrt0TjpqoqmZmZHDx4kL1797Jjxw6Kioqq3tQ7g7sflBrr5FxFRUUsX778tO/7+fnRokUL+6NZs2YEBgY6zDIRQghRv8xmM5mZmRw/fpwjR45w6NAhkpKS/pk5qNGieAeDqkJh5iWJISUlhfHjx+Pp6YnVasVstmCxmLFabaioaBQFvV6Pi4sLbm5ueHp64u3tjZ+fH/7+/vaHwWDAy8tLvg8KIS4LSYaI8/bwww/j7+/PSy+9hIeHBydPniQzM5Nx48ZhNBo5evQor776KlD1j6lqOTk5zJw5kwkTJuDl5cV7773HAw88wJo1a/D396+v7ghRr1RV5ZNPPuGvv/5CVVWKioqoqKj4p4KzG0pIGxRDcwBsCb/UbQCKBlw8qr4ko4Lt75/O7hhLyzFu38727dvt1XV6Pb4+PvYvqnFxcYwaNQq9Xl+3cQkhhDitn376iVWrVlFRUUF+fr7jrA9FA54GFJ8mKD4hKN5BKFodlu0rLmlMhYWFFBYWwanLaqqfq/b/gAKcYQaKRqPB29sbJyenv5tQUFWV6OhoRo8ejZeX1yWJXwjR+EgyRJwXo9FISkoKTz31FNdddx0A3bt3t7/v5+dHenq6w2yPagUFBbz++uvEx8cD0LVrV/r06cPSpUuZPHnyZYlfiCuN0Wjkm2++qfmGokUT0QkltB2KrirRYN33M9gsdRuAaoPyopqn9/RH0+lm1MIs1IxDqNnJAFjMZnJycuz1vvvuO/r06UOrVq3qNi4hhBCn9cMPP5Cenu5YqGhQmkShCYkGd98z7vVx6ah/J9f/eVmDTyjaFl1RS42oxbmoRTlQ/M+4YrPZyM/Pr3FYVlYWnTt3pnfv3pcgbiFEYyTJEHFefH19CQ0NZd68eRQWFhIfH09wcPA5Hevp6WlPhAB4eXnRvXt3du/efYmiFeLK5+/vz8MPP8zBgwcpKysjPT2dtLQ0bDYrtmPb4cTOqit7Ac1RgqNQS4xgKq27ALR60FVdfUNVq5Ijqopako91w9Kq139TFIWgoCCCg4Px+Xt2SMuWLWnevHndxSOEEOKs7rzzTv766y9yc3PJzs4mLy8PVBtqeiLW9ERwcquaGeIbUvVw8UTxD0ctybvMkVYnZE7JiuSnYd2RVmttnV6Pn68vvr6+eHt74+7ubt8UPDg4mLi4uEsfshCi0ZBkiDgviqKwZMkSXn/9dZ5//nnKyspo164d06dPp2vXrmc89tQlM9X8/f05fvz4JYpWiKtD//796d+/v/21yWTiyJEj7Nmzh+3bt3P06FFUY2rVdGPPwKrpxebyujm51Vz1+Bc3Jy3hUZE0b96cFi1a0Lx5c8LDw+3TloUQQtSf7t27O8zMLS8v58SJE/Y9QxL27yc/O9k+qw9XL/AMACc3qCyr83hcXFxo06YNGo0Gi8WC2WzGarVitVpRVRWNRoNOp8PZ2Rl3d3e8vLzse4ZU7xtiMBjw9PSspxktQojGSJIh4ry1aNGCN998E7PZzK5du5g3bx5jx47ljz/+OONxRmPNTR/z8vIICAi4VKEKcVVydnamXbt2tGvXjjvvvJPs7Gw2b97Mxo0bOXTo0L/uCKA4rM+eNu1JapuXrCgKWq0WjUaDh4cHYWFhdOvWDTc3N7RarcPdZOQLqRBCXF1cXV1p06YNbdq04eabb0ZVVdLT0+13k0lISKCkOjHyt+q7yFTN3qhtPcs/Y8epD41GYx8f5Na6QoirmSRDxAXT6/XExcUxZswYHn74YbKzs9Hr9ZhMplrrFxcXs2nTJvtSmeLiYjZv3szdd999OcMW4qoTGBjIrbfeyq233kphYSF79+4lMTGRY8eOkZGRQXFxMVarFZ1Oh6eXF/5+fri7u9O+fXuioqJo3rw5Hh4e9d0NIYQQl4miKISGhhIaGsrAgQOx2WykpqaSlJTE8ePHycjIIC8vj8LCwhrJ8JCQEJo1a0aLFi3w8/OTxLgQosGSZIg4LwcPHmTOnDkMHDiQpk2bUlJSwuLFiwkNDSU8PJyWLVuyfPlyvv/+e5o1a4avry9hYWEA+Pj48L///Y9HH30UT09P3nvvPQDuvfdee/tpaWns27cPqJryefLkSX766ScABgwYcJl7K8SVx9vbm169etGrVy+HclVV7V9Yy8rKSExMJDo6Gjc3t/oIUwghxBVEo9EQHh5OeHi4vUzGCiFEYyfJEHFeAgICMBgMLF68mKysLDw9PenSpQuvvPIKWq2WYcOGsXfvXl544QUKCgoYPHgwL7/8sv3Yxx9/nLlz53Ly5Elat27N+++/j8FgsLe/ZcsWpk+fbn+9YcMGNmzYAMChQ4cub2eFuIrIlTshhBBCCCHOnSRDxHnx9/fnlVdeOe37Hh4ezJs377Tv9+3bl759+572/SFDhjBkyJCLCVEIIYQQQgghhDgjTX0HIIQQQgghhBBCCHE5STJECCGEEEIIIYQQjYoskxGXRfW+IUKIumcymSgoKECv1+Pj41Pf4QghhBBCCHHFk2SIEEJcZVRV5eTJk2zatIlt27Zx9OhRVFUFwM3dnY6xsbRu3Zro6Oh6jlQIIcTlpqoqeXl5pKamUlJSglarJSAggGbNmqHX6+s7PCGEuGJIMkQIIa4CVquVPXv2sHXrVnbs3ElOdnbVGxoNeAWhuHqBzUpZcQ4bN25k48aN7Nq1i4cffpgmTZrUb/BCCCEuqcrKSnbv3s22bdvYuWsXebm5Neo4OzvTpUsXbrzxRjp06FAPUQohxJVFkiEN1IoVK3jyySf5+eefiYiIsJd/+umnPP/884wdO5ZJkybZy0tLS+natSuqqmKz2c7Y9vjx45kwYQIA69at45lnnuG3337DycmJLVu2MGrUKL7++mvat29/UX1ITU3l5ptv5rvvvqNp06YX1ZYQVyur1cqaNWv4evnyf77cKgpotKBoq5IhZYWoZYX/HOTmAzYLe/fuZeLEidx///0MGDBAbr8rhBANRHl5OR9//DE7duwgPz8fs9nsWEHRVo0V1b/3NVpMVht//fUXf/31F81btGDQbbfh7u5++YMXQogrhCRDGqhrrrkGgJ07dzokQ3bt2oWrqys7d+50qL9nzx6sVivTpk2jc+fO9vLx48fTuXNnRo8ebS8LDg4GqqZhzp8/n/vvvx8nJ6c670NYWBg33HADCxYsYO7cuXXevhBXuiNHjvDWW29x7Ngx0OhA5wwWE6gqqFbAypxZcwCYNm3aPweay6t+OrtTabWwePFidu3axSOPPIK3t/fl74gQQoiLoqoq2dnZHD58mD179vD777/XTID8bc6cWsYF6991XTzBw59jR48xf/58goKCGDFiBL1790ar1V7qbgghxBVFkiENVHh4OAEBAezcuZMhQ4bYy3fu3MngwYP55ptvMJvN9rWjO3fuRKfTMWLECNzc3Oz1nZycMBgMdOzYscY5Nm/eTHJyMoMHD75k/Rg6dCgPPPAA06ZNw9/f/5KdR4gridFo5Ouvv+bHH39EVVWUJlGg1aOmJpxfQ6ZSlLAYKM1n69atJCYmcvfdd3PdddfJunEhhLgCVFZWUlJSQnl5ORUVFZSXl1NWVkZxcTFGo5Hs7GzS09M5fvw4paWlF3/CimKUoFZoWnTFdmIXWVnJvPHGG3z++edcf/31xMfHExYWJjMJhRCNgiRDGrBOnTo5zADJysoiLS2Ne+65h6+++orExET7mtGdO3cSHR3tkAg5m5UrV9K1a1f8/PxqvFdYWMiUKVP47bff8PHx4c477+TBBx90qPPrr7+ycOFCkpOT8fLy4sYbb+Txxx93mLIZFxeHt7c33333Hffdd995fgJCXD1UVWX16tXs3LnTPlMLNx80TaKw5Z6A4pwLazf9IJqYG1D8wyk+tp133nmH//u/T+nS5Rq6detG9+7d67gnQgghVFVly5YtHDlyBACbzUZxcTGFhYXk5+dTUFBAYVERlSbT2RtTFHD1Bp8QqCgGUymoZ17SfMbYUvah+jRBG90XtVknbKkJZGcd4bPPPuOzzz7D19eXli1bEhISQtu2bWWcEEI0WJIMacCuueYafvnlFwoKCvDx8WHnzp0EBgbSokUL2rVrx86dO+nQoQM2m43du3czbNiw82p/06ZNpz1mxowZ3Hbbbbz11lusWbOGV199laioKHr37g3A2rVrGT9+PAMGDGDSpEmkpqby2muvcezYMT766CN7OxqNhtjYWDZu3CjJENGgpaens2TJEvtrJSwGTYs4bPt/gcLMC2/YZkFNS0Dbvj9KQHOsu1dTXFzIunXr+OOPP/jyyy9larQQQtSxtLQ0Xn755XM/QKMFJzdwckVxdgdXbxQPfxQPX3DxRNFose77GbWi+OKDs1lQU/eBbwiKmzfayB6oLeJQUxOwHa/ag2T79u0AfP/99yxbtkxmEwohGiRJhjRgnTt3RlVVdu7cyXXXXceuXbvo1KkTUDVrZMeOHdx3330cPnyY0tJS+z4j5yI7O5usrCwiIyNrff/GG2+0b7LavXt31q9fz88//2xPhixcuJD27dvz+uuv24/x9vZmypQpbNmyhW7dutnLo6Oj+fTTT8+3+0JcVQIDA+nRowd//fUXQNWXUnMFSnAkqsUMZYX/7AVyPpzdUcLaoxZmYz38J5RXbbSq0+kYNGiQJEKEEOISaNKkCbfeeis//fSTQ7mqgsVitt8O3c5mrZr1UVFM9TsqgM6pKjHi7gvOVft9YCq7sPGg2t/jAoBqs6JmH8WWnghF2fYqGo0GnU7HLbfcIokQIUSDJcmQBiw6Otq+Wep1113Hzp07ufnmm4GqZMh3330HwI4dOwAcNk49m5ycqin7tS2RAejZs6f9uUajoUWLFmRmVl3dLi0tJTExkSeeeMLhmAEDBjBt2jS2b9/ukAzx9fWloKDAYY8TIRoavV7P1KlTmTx5Mrt27eLzL74g+cgRyEtB0zIO/MOx7V4NZQXn3qibD5rY/6Km78d2Yg+KAtf168egQYPkDk1CCHEJabVaRo8e7bABfTWr1UpRURGFhYUUFxdTWlpKWVkZFRUVVFRUUFpaSnFxMfn5+fY9Q8ynLpVUNFWbatss5x+Ymw/aTjeDVo8t7QC2k3vAVIpGo6HTNddw7bXX0qFDBwICAi6i90IIcXWQZEgDptfrad++PTt37qS8vJyDBw/y7LPPAlXJkNzcXE6ePMnOnTvtG66eK9Pfa1xPdxcZT0/PGrGUlZUBUFxcjKqqGAwGhzo6nQ4fHx8KCwsdyp2dne3nlGSIaOi0Wi1dunShc+fOrFmzhqVLl1J+aAN4+FdthlqWj1qYBRVVG+lNe3pG1YF6138acXFH8WmCEtiyaplNUTZBwcFMeuwx2rRpc/k7JYQQwk6r1eLr64uvr+851bdarWRmZpKcnMzhw4dJTEzk6NGjOMwt0Wj/vuW65szjQlh71Kxk1JS9qKZS9Ho9N/73vwwaNEgSIEKIRkeSIQ1c586d+fDDD9mxYwdarZbo6Gigakp+aGgoO3fuZNeuXcTFxZ1Xu9W35ywqKjrvmDw9PVEUhby8PIdyi8VCQUFBjVt/FhYWotfr8fDwOO9zCXG10mg0DBgwgLi4OD755BPWr1+PevhP0GhRvJugNGuFYohAcan5/4VqqaxaZrPrO7BZ+c9//sOYMWNwdXWt5UxCCCGuZFqtltDQUEJDQ+3LjcvKykhISGDnzp1s276dvNzcqqU2Wh2KTwiKTzC4+6M4uYDNhlpeiJqfjm3b12CpxNnZmRtvuYXIyEiuueaa89pAXwghGgpJhjRwnTt3ZtGiRXzyySe0b9/eYWZFp06d+Omnn0hLSzuv/UIAmjZtil6vJzU19bxjcnd3Jzo6mh9//JH777/fXr5mzRosFgtdunRxqJ+amkrz5s3P+zxCNAR+fn5MnDiR4cOHs27dOrZu3cqxY8dQ81PhyGbwNFR98XX1qtoUrzgXNfcEWM34+Pjy0ENjiI+Pr+9uCCGEqENubm7ExcURFxfHQ6pKcnIymzdvZsuWLaSknETNO1nrcQEBAdxwww0MGDAAnU5HYmLiZY5cCCGuHJIMaeA6d+6MRqPh999/r3Fr286dO/PCCy/Yn58PJycnYmJi2L9//wXFNX78eB555BEmT57MoEGDSElJYd68ecTHxzvsFwKwb9++807WCNHQBAcHM3LkSEaOHInRaGT79u1s3ry56ja8xbkO06UDAgLo0KEDd91112n39RFCCNEwKIpCq1ataNWqFXfffTd5eXkcPHiQ1NRUiouL0el0BAYGEhUVRYsWLVAUBcC+fFkIIRorSYY0cJ6enrRq1YrDhw/b7yRTrVOnTqiqio+PDy1atDjvtvv378+HH36Iqqr2gfVc9evXjwULFvDWW28xbtw4vLy8uPXWW3n88ccd6uXk5JCYmMjUqVPPOz4hGio/Pz9uvPFGbrzxRsrLyzl69Ci5ubno9XqaNm2Kn58fBw8exMXFpb5DFUIIcZn5+/vTo0eP+g5DCCGueJIMaQSq7xrzb23btuXQoUNnPPa333477Xu33XYb8+bNY/v27XTt2hWAbt261drm4sWLa5TdcMMN3HDDDWc8/48//khYWBjdu3c/Yz0hGitXV1fatWvnUCZX+4QQQgghhDgzTX0HIK5efn5+jBw5kg8++OCStG+1Wvnkk0945JFHznvmiRBCCCGEEEIIcTqSDBEXZezYsbRr147Kyso6bzs7O5uhQ4dy66231nnbQjR0qqpSVFQks0SEEEIIIYSohSyTERfFz8+P8ePHX5K2mzRpwtixYy9J20I0VEajkWXLlrFhwwZ7IiQ0NJR+/fpx0003ye11hRBCAFBUVMSOHTtYs2YNWVlZWCwWvL29admyJV27dqVdu3ZoNHLdVAjRcEkyRJyV0WgkPj6e2bNnM2TIkHM65ptvvmH69Ols2rTJfjcLo9HI66+/zoYNGzAajYSGhjJ8+HBGjRplXwZTWVnJ66+/zp49e9i/fz/l5eUObQghaqeqKj///DMfffQRFRUV4OSGYogAm4W0zEw+/vhjvv12FaNH30/v3r1l6ZkQQjRSGRkZfP3116z//XesFktVoc4JNFpS09LYv38/q1atIji4CYMHD+K6665Dr9fXb9BCCHEJSDJEXDYTJkzgxIkTTJo0iZCQEDZu3MisWbOw2Wzcf//9AFRUVPDVV1/Rvn17rrnmGv788896jlqIK19BQQFvvPEGu3btAkUBrR5UFbUwq6qCRgdaJwqLCpk/fz7r169nzJgxNGnSpH4DF0IIcdlUzxxcs2YNqqqCRls1Xmi0gAIqoHP5OzGiITMri3feeYevly9n5IgR9OnTB61WW9/dEEKIOiPJEHFZZGZmsn37dmbNmsXQoUMBiI+P5+DBg/zwww/2ZIiXlxdbt25FURS++eYbSYYIcQZWq5XffvuNj5YupbSkpKpQVcFqZs6sFwGYNm2a40FaHbt27WLCo48y8KabuPXWWzEYDJc5ciGEEJeL0Whk1apV/PDDD1V7vCkaQAWbFbDWHC/M5VU/Xb3BN4SczMO8+eabLF++nDvuuIOePXtKUkQI0SBIMkTU8OWXX7Jo0SLy8vLo2LEjkyZNcnh/5cqVLFu2jOTkZFRVpU2bNkydOpUOHTqctk2z2QyAp6enQ7mXlxf5+fkOZTJ9X4iarFYrxcXF5Ofnk5mZycGDB/nzr7/Iy839+4vtuTZkgYDmWItzWbVqFd999x3t2rWjXbt2hIeHExAQgK+vL76+vuh0MkQIIcTVyGw2k5CQwPr16/nrr7+wWCzg4gH+wZB38twaKS9ECWyBptsd2I7vIi39MPPnz+fjTz7h+n796NmzJ2FhYfK9TQhx1ZJvusLBunXreOaZZxgyZAgDBw4kISGByZMnO9RJTU1l0KBBhIeHU1lZyffff89dd93FqlWraN68ea3tNm3alB49erBo0SIiIiIICQlh06ZN/PLLLzz//POXo2tCXNEKCgpITU2lqKiIvLw8cnJyyM7OJjs7m9zcXIqLi6umNZ9K54QS2g41Px3K8mtvuDZ5KSgx16NUlmHLOERCQgIJCQkOVRRFwcfHh6CgIIKDgwkKCsJgMODr64u3tzctWrSQK4NCCHEOsrOzKSgowGQyUV5eTnl5OSUlJZSWltp/lpeXU1lZab94pNFo0Ov1uLi44ObmhpubGx4eHri5ueHq6oqzszN6vR5FUbBYLFRUVFBYWEhWVhYnTpzg8OHDmEwmABR3XzQt2qEEt8a6c9V5xa5mJ6M0vwZtVE/UZh2xpewjLyuJZcuWsWzZMvwNBtpERREeHk7Tpk3p1q2bjA1CiKuGJEOEg3feeYcuXbowe/ZsAHr16kV5eTmLFy+21zn17jE2m40ePXqwb98+VqxYUSNxcqqFCxcyadIkbrnlFqDqH1tTp05l0KBBl6YzQlwlrFYrjzwyntLSktNX0ruguPuCmy+Kqyd4+KN4B6FotFi2rzi/E9osqGn70bbvjyY4EtVcgVqcC2WFqKYSKM1HLc4lPz+f/Px8Dh48WKOJO++8kzvuuOM8eyqEEI3L4cOHeeKJJ+rl3EpIWzRBrcAr4MJnb1SW/9Oeiwfa1vGoLbqg5p7Elp5IXm4mf+Xm8tdffwHw7LPP0rlz57oIXwghLjlJhgg7q9XK/v37mTp1qkN5//79HZIhycnJzJs3j127dpGXl2cvP378+GnbVlWV6dOnc/z4cV599VWCgoLYunUr8+fPx8vLi9tvv73O+yPE1UKr1dKnT29++OGH01cyV6AWZEBBBqqzB4qHH5QVQmALFP9w1JK80x/7b87uKGHtq2aaFOeiGlNQi7JRy4qgsgRstjMertfradeu3bmfTwghGqmgoCCio6NJTEy87OdWsw5jM1egNIkE31AURTn/8cLQzLFNSyVqZhK2rCNQnOPwnouLC0FBQXURuhBCXBaSDBF2RqMRi8VS4za2p26uWFJSwujRo/Hz8+PJJ58kJCQEZ2dnnn76aft0zNqsX7+en376iW+//ZY2bdoAEBcXR1FREXPnzmXo0KFyL3vRqI0ZM4YxY8YAVYlJo9HosEymoKCAoqIi8vPzycjMJC/3JGreSTi6FSWkLbj5QFnB2U/k5oOm481QkI51+wooNdrf8vXzI7BZa/z8/PDz88NgMNiXyQQGBuLh4XFpOi+EEA2Ut7e3fbbtqVRVpaKigpKSEsrKyigvL8dkMmGxWFBVtcYyGXd3d9zd3c94i1ur1UpeXh7Hjx8nMTGRbdu2kZp6FDXnKLj7omnWESW0LWrOsXMeL7StulfFW1mOLWUfanoiWM1otFo6dOxI586diYyMJDw8HDc3twv9mIQQol5IMkTY+fn5odPpMBqNDuW5ubn257t37yYzM5PFixfbkxoAxcXFBAcHn7btI0eOoNVqiYqKciiPjo5m6dKlFBQU1EjCCNFYabVaAgICCAgIOO0MjNzcXDZs2MC3335LQcoecPNBCWqNWpYPFaVMe3pGVUW9a9VPF3cUnyYoAc2x7f8FCrPQaLVc27MnvXr1ol27dpLsEEKIy0RRFFxdXXF1da2zNrVaLYGBgQQGBhIXF8eoUaNITk5m9erV/P7HH9gOrAN3P5SIa6AoG7Uw44zjhaZZJwCsx3agpiaA1Yyfnx+33HIL1113Hd7e3nUWuxBC1AdJhgg7rVZL27Zt+eWXX7jvvvvs5T///LP9eUVFBYDDlYmdO3eSlpZG69atT9t2aGgoVquVxMRE2rZtay9PSEjAzc0NX1/fOuyJEA2fwWBg8ODBDBgwgP/7v/9j9erVqBVFaCK6oDSNQfnXHWZUqwXbyT3Ydn0Pqo1rr72WUaNGnTGJKYQQ4uqlKAqtWrVi4sSJjBgxgi+++IL169ejHlgL3kFoIq5B8av9bjBqZQVq6n7U1ARUiwlfXz/uuON2rr/++jPOThFCiKuJJEOEg7FjxzJu3DimT59uv5vM999/b3+/Y8eOuLm5MXPmTMaMGUNWVhYLFy486xrRPn36EBoaysSJE3nkkUcICgpi8+bNfP7554wePdphIP79998pLy+3391i3bp1uLu706pVK1q1anVpOi7EVcrV1ZUHH3yQLl268Mabb1JwdCtkHUETHoviHVy1WWreyaovtKZSAgICePjhh2WDOyGEaESCgoKYOHEigwcP5rPPPmPz5s3Y9v0MLp4o/uFV+1BpdFBZilqQiZqfCjYbnp6eDB06kptuuglnZ+f67oYQQtQpSYYIB/369WPmzJksWrSI1atXExsby2uvvcaIESOAqqvRb7zxBnPnzmXcuHFEREQwY8YMlixZcsZ23d3dWbp0KfPnz2fevHkUFhYSFhbG5MmTuffeex3qzpw5k7S0NPvrp556Cqi6i82ECRPquMdCNAydOnViwZtvsnTpUtauXYstcZ3D+07Oztx2++0MHToUFxeXeopSCCFEfQoPD+fJJ5/k+PHjrFy5ko2bNlGZtp9/3bidiIgIbrzxRq677joZM4QQDZYkQ0QNI0aMsCc/qh06dMj+vHfv3vTu3dvh/T59+ji8HjJkCEOGDHEoa9q0KfPmzTvr+X/77bfzDVkIAXh6ejJ+/Hj++9//smrVKiorK3FxcSEyMpJrr70WT0/P+g5RCCHEFSAiIoIxY8Zw7bXX4uTkRH5+PpWVlXh7e9OyZUsCAgLqO0QhhLjkJBkihBANTFBQED179iQ6Olp29xdCCHFaWq2WyMhIGSuEEI2S3MtUCCGEEEIIIYQQjYokQ4QQQgghhBBCCNGoSDJECCGEEEIIIYQQjYokQ0SdSU1NZcGCBWRlZV3Q8UajkRdffJHbb7+dmJgYOnXqVMcRCiGEEEIIIYQQsoGqqENpaWksXLiQvn37EhQUdN7HZ2Vl8cMPP9ChQwdiYmIc7mAjRGNXXl7OsmXLSEhIIDc394x1VVXFYrGg0+lQFAWDwUBMTAzDhw/H1dX1MkUshBDiUqkeE/bt20dOTg6qqqKq/9wgV1EUh8e/GQwG2rRpQ9u2bS9n2EIIcUWRZIioE6qqUllZeVFtREVFsXHjRgAWLFggyRAh/lZYWMj06dNJT08/a905c+YAMG3aNHtZQUEBR44cYd26ddx3331ERkYSHByMVqu9ZDELIYSoG1arlczMTFJSUkhJSeHEiRNs3bq1xveu2n7/n071uPDXX3/x0ksvyd1khBCNkiRDxAV58sknSUhIYOrUqbz22mscPXoUs9kMwLBhw+z1qhMaSUlJzJgxg7179xIUFMT48eP58ccfKSsr45NPPgFAo5FVW6JxKi8vJzMzk6ysLLKzs8nJySEnJwej0YjRaCQvL8/hit+FKiws5I033gBAp9MREhJCaGgoISEhNGnShKCgIAIDA/H390enk+FBCCEuJ4vFQlZWlj3pkZKSwsmTJ0lNS8Py93esupafn8+4ceMICgqiSZMmBAcHExwcTGBgIAaDAX9/f3x8fCR5LoRokOTbrrhg2dnZvPTSSzz88MN4eXmRmprKrFmzmD17Ni1atLDXq6ioYPTo0Xh5efHKK68AVTM/ioqKiIiIqKfohbi0VFWloqKCiooKysrKKC4upqCggPz8fHJzc8nJySErK4vMzEwKCwtP35CigTpIhPzTngJaJyw2KydPVn3Rro1OpyMoKIiwsDD8/Pzw8fHBx8cHLy8vPD098fDwwNXVFRcXF1xcXNDr9ZLQFEKI0ygpKaG0tJSSkhL7eGA0Gh3GgqysLKxW67+OVECjBZ1z1U+NDkyloP673sXJys4+455vGo0GrVaLRqNBr9fTokULwsLCcHNzw93dHXd3d/tzV1dXh/HByckJvV6PVqutdcmOEELUF0mGiAtWWFjIkiVL6NChAwBbtmwBoHXr1rRv395eb/ny5eTm5vLZZ5/RtGlTANq2bUv//v0lGSIarCeeeIKkpKSzV3T1RvFrilqWDxUlNd9XbXUbmKqCxXTWahaLhbS0NNLS0s656ZtuuomHHnroYqITQogGZ82aNbz99ttnrqRzBg8DipsPips3uPliS9kHBelgs1Q9LqWzJN1tNhs2W9V4ZDKZ2Lt3L3v37j3v07z66qu0atXqgkIUQoi6JskQccF8fX3tiZAzSUhIICoqyp4IAQgPD6d169aXMjwh6pWvr++5VawsQ9XqwMn9ny+jihY0mqpZIYoCpjIwl9dNYHpXcD5lbbjN6vhQbf/8PE9eXl51E6MQQjQg57Qfh7USTOWgdULVOqHonNEYIrDZrDUTIXU5JsA/44LNVjXjxGb9+3n1a9vf49PFzVJ0cnLC2dm5bmIWQog6IMkQccH8/f3PqV52djZ+fn61Hm+xXOIrHULUk6eeegqoWi5TXl5OcXExhYWF9j1AcnJy7NOiMzIyqKioqL0hjbbqi6pGW/UF9WI4uYEhHKWiBLWsoPaZKIC3t7d9rbifnx9+fn74+vri7e3tsEzGzc0NvV5/cTEJIUQD17NnT3r27EllZSWlpaUUFRU5LJPJzs4mIyOD9PR08o0pYExxTDs4u6O4+4KbT9VPvSu25C1QfoYlludKq6t6lObXmgRXFAUvL68av/+rl8b8e4mMm5ubfZmMq6urjBFCiCuaJEPEBTvXdZ+BgYEkJibWKM/Ly8Pb27uuwxLiiqIoCm5ubri5uZ32ltOqqlJUVERWVhZZWVnk5OSQm5vrsIFqfn4ZZ5+roTBt2pP25zWu4lWWQfpBVKqSkWFRLQkLCyMkJMS+aV5AQABOTk4X1WchhBA1OTk54eTkhK+vL82aNau1TllZGampqZw8efKfu8ecPElebioYUx1/qyvKKftKKUybXpWER/P3ZqfVsw3PNKvDasHLQ0No8yiCg4MdNtM2GAz4+fnJ5qlCiAZLkiGizlRn/00mx/0IYmJiWLlyJSkpKfalMidPniQpKYkuXbpc9jiFuNIoioK3tzfe3t5ERkbWWsdms1FYWEheXh65ubn2n9UJk+LiYkpLS6msNFdNq6Zq2YqPjw/+/v4EBQXZ7x5TvemdEEKIK4ubmxuRkZE1xoLqJElKSgppaWlkZGSQlZVFbm4uxcXFqKqt1nyHTq/Hx9sbX19f/P39MRgMBAYGEhgYiI+PD/n5+cTGxsqYIIRolCQZIupMREQEWq2W5cuXo9Vq0el0tG/fnqFDh7Jo0SLGjh3Lo48+iqqqLFiwAIPBUGN2yU8//QTAkSNHsFqt9tft27cnNDT0svdJiCuFRqPB19cXX1/fs24+V1ZWRmJiItHR0fIFVwghGoDTJUkArFYrpaWlmEwmLBYLGo0GJycnXF1dcXZ2Pu1M3rKyMkpLSy916EIIccWSZIioM35+fjz77LMsWbKEVatWYbFYOHToEC4uLnzwwQc899xzTJkyhaCgIB555BGWL1+Op6enQxsTJ06s9fXs2bMZMmTIZeuLEEIIIcTVQKvVygbWQghxASQZIi7Iyy+/XGv5iBEjGDFiRI3y1q1b89lnn9lf5+fn89JLL3H//fc71Dt06FDdBiqEEEIIIYQQQvyLJEPEZfHuu+9iMBgIDQ0lJyeHDz74AJvNxtChQ+s7NCGEEEIIIYQQjYwkQ8RlodVqWbRoEZmZmWi1WmJjY1m6dClNmjSp79CEEEIIIYQQQjQykgwRl8UDDzzAAw88UN9hCCGEEEIIIYQQaOo7ACGEEEIIIYQQQojLSZIhQgghhBBCCCGEaFRkmYyoF1u2bGHXrl2MHTvWodxoNPL222+zZ88eEhMT0ev17Nq1q56iFEIIIYQQQgjREF1UMsRkMrF//34KCgrw8fGhXbt2ODs711VsogHbunUrH3zwQY1kSFZWFj/88AMdOnQgJiZGbrUrxDmy2WxkZWVx8uRJkpKS2LBhA8XFxZhMJlRVdairKIr9odFoHB4Gg4HY2FiGDx+Oq6trPfVGCCFEXbFYLOTm5pKWlsaPP/7I8ePHKSkpQVVVbDYbGo3GYVw4dXyofn4mBoOBmJgYGTeEEFedC06GfPjhh7z99tuUlJTYy9zd3Rk3bhyjR4+uk+BE4xMVFcXGjRsBWLBggSRDhKiF1WolNTWV5ORk++PYsWOYTKazHjtnzhwApk2bVuv7xcXFHDt2jO+//562bdsSFBSEwWDAz88PX19f/Pz88PHxwcvLC51OJhcKIcSVoLKykuzsbDIyMuyP9PR0MjIzycnOrpEU/7ezjQ1nUlBQwJEjR/j111/p27cv3t7euLm54eHhgZeXF97e3vj4+ODt7Y1Wq72g/gkhxKVwQd9kP/nkE+bMmUOPHj24+eabMRgM5Obm8t133/HKK6+g0+kYNWpUXccqrhBffPEFixcvxmg00vH/s3ff4U1W7QPHv0madO/JpgzDpmW17C1DUEBkiAVEhqCgIAgIgqCykSlLhgjoD9lLQQEZrywHCChbRimdtKU7zfr9wdu8hJbSltKWcn+uKxfNec5znvsJ0JPcOSMggFGjRtGjRw+mT59Ot27d2L59Oxs3buTatWuYzWaqVKnCmDFjqFWrFnA/ybF48WLgfvIDoEGDBqxbtw6lUpaxEQLuf5OXkJBAbGwsUVFRREREcPv2bW7dusXNmzfR6/X/q6xQgqM7CreymHXJEH8nX65/9uzZbOs4Ojri6upqeWQkSTKeu7i44OzsjLOzM46OjtjZ2T32G0YhhBBgNptJT08nNTWV5ORkEhMTSUxMJCEhgfj4eOLi4oiNjSUmJobo6Gji4uKybsjWEVx8wZAOybFPNeakpCR27979yOMKhQI3Nzc8PDwsCfaMx4N9hpOTEw4ODtja2kqfIYR4qvKUDFm7di0vv/wys2bNsirv2rUro0eP5ptvvpFkSDF14MABJk+ezGuvvUa7du24cOECH3zwgVWd27dv06VLF8qWLUt6ejq7d++mT58+7Ny5E39/f1577TUiIiLYvXs3a9euBcDJyakwbkeIIiUsLIwxY8aQkpLy6EpKm/uJDy8PFM5eYDZjivoXjHrMybGQEp9/ASlVYOcMZhOYTPf/tDzMJKemkZySwp07eU++TJkyhdq1a+dfzEII8QzZtGkTGzZsyHsDCiUoFKBS3/9ZqbJ+ABj1kHovfwLOSTx2TmA2W/qKjH7DbDIRdy+BuHsJXLt2LU/NazQapk2bRqVKlfI5cCHE8yhPyZCoqCg6d+6c5bFXXnmFn3766YmCEkXX0qVLCQ4O5rPPPgOgadOm6HQ6y0gPgHfffdfys8lkonHjxpw7d45t27YxatQo/Pz88PPzQ6lUEhAQUNC3IESRde/evewTIYDCxQecPFE43U+GGK+dgnsRTycgkzF/kytZiIiIkGSIEOK5ldekAAA2/12nz6ADjPkSzxMzmyA1Ifs6ajvQOELaY+plIT09nbt370oyRAiRL/KUDClfvjx3797N8lh0dDTlypV7oqBE0WQ0Grlw4QIffvihVXnr1q2tkiHXrl3jiy++4PTp01b/Tm7cuFFQoQrxTKpWrRqrV68mPj6epKQkEhISuHv3LtHR0YSHh3P79m2iosIxx9/BMvtbqbz/jaBSdX/UiFH/3zfG+UEBCu5/s/cYKpUKDw8PfHx8rKbJuLi44OTkhKOjI05OTtjZ2WFra2t5ODo65lOsQgjx7Bk7dixJSUkYjUb0ej3p6enodDrLFJmUlBSSk5MtfULGNJn4+HjuxsZyLz7+ES0r/tsv/He0iNFwv38oCvRpOKiVeJQujbu7u2U9EVdXV5ycnCzTZDKmytja2qLRaFCr1dja2soirUKIfJOnZMiIESOYNm0a1apV44UXXrCUX7x4kcWLFzN+/Ph8C1AUHbGxsRgMBjw8PKzKPT09LT8nJSUxYMAAPDw8GDduHCVLlsTW1paJEyfmaHFHIZ53GXOpHyUtLY2bN29aLZ56KzQUkz7/3+SqNWp8vL3x9PS0xOXm5maZ353xcHZ2lkXxhBAiDxQKBc7Oznk+X6/XExMTY1lbKiIi4n+Lp4aHo9en52O02VOr1ZQrV84qAf7gAqoZi3B7eHjI7pNCPKGtW7cyfvx4Nm/eTM2aNQvsuhlrPx4/fjzb96vPijwlQzZv3ozRaKRLly5UqlQJb29voqOjuXr1Kj4+PmzZsoUtW7YA93/JL126NF+DFoXDw8MDGxsbYmOtF+B6cPTHmTNniIiIYPny5VSpUsVSnpiYiJ+fX4HFKkRxZWdnh1artSw+DPeHDd+6dYsbN24QGhrK9evXuXLlCjqdDpPJZHX+gzsFZLW1rqurK1WqVKF37974+PjI4nVCCFGEqdVqSpQoQYkSJTJNOTSZTMTGxhIREUFkZCTh4eH88ccfREVFWbZdN5vNKBQKJkyYgEKhQKPRZLm17sOPB8nWukKIZ1WekiGXL19GpVLh5+dHUlKSZXvdjA+7ly9fttSVN9LFh0qlomrVqhw4cIB+/fpZyvfv32/5OS0tDbjfOWf4888/CQsLo3LlypYytVpNenrBfVshRHGm0WioVKmSZQ51SkoKFy5coGrVqmg0GlJTU9Hr9ZjNZmxsbLCzs7O84RVCCFE8KZVKvLy8LMkKgDfeeMNy/MG+wsHBobDCFEKIQpOnZMjBgwfzOw7xjBg6dCjDhg1j4sSJtG/fnn/++YcdO3YAWBZEdXBwYMqUKQwePJjIyEgWL16Mr6+vVTsVK1bEYDCwdu1aAgMDcXJyokKFCgDs3bsXgKtXr2I0Gi3Pa9asSalSpQrwboV49tnY2DzREGwhhBBCCFHwrl27xpdffsmJEydISEjAy8uLBg0aWDayAEhOTmby5MmWz0v169fn448/zvTZ64cffmDt2rVcunQJhUJBnTp1+OCDD6hWrZpVvb/++oulS5dy+vRpUlJS8PX1pWXLlkyYMCHbOAcPHoyHhwfLli2zWkKhqFMWdgDi2dK6dWs++eQT/vOf/zBs2DCOHj3K5MmTgfvb43p5ebFgwQJiY2MZNmwYa9eu5ZNPPsm0qG7Lli15/fXXWbFiBT169LC0AfDee+/x3nvvsXfvXnQ6neX5yZMnC/RehRBCCCGEEKKgXbx4ke7du3PmzBlGjBjBV199xahRo0hPT7caXT9x4kTUajVz585l9OjRnDp1ijFjxli1tWzZMkaNGkXFihWZP38+s2bNIjk5mT59+nD16lVLvaNHj9KnTx/u3LnDuHHj+Oqrrxg6dOgjN04BOHXqFL1790ar1fLNN988U4kQyOPIkDt37jy2TsmSJfPStHgG9O7dm969e1ueb9q0CcCyRkizZs1o1qyZ1TnNmze3em5jY8PkyZOtkiAZLl26lN8hCyGEEEIIIcQzYfr06djY2LB582arhUpffvllq3pNmzZl4sSJluf37t1j9uzZREdH4+3tTXh4OIsWLeKNN96wqteoUSPatWvH4sWLmT9/PgBTp06lRIkSbNq0yWqR41dffTXLGHfs2MGECRPo1asXH330EUrlszfOIk/JkFatWj12rvmFCxfyFJAo2uLj41m8eDHBwcE4Ojpy7tw5li1bRuvWrSldunRhhyeEEEIIIYQQz6zU1FR+++03unfv/tgdW1q1amX1PGOB/Tt37uDt7c1//vMfDAYDr7zyCgaDwVLP1taW+vXrW0beX79+nVu3bjFq1Kgc7fa0bNky1q9fz4cffkj//v1zeYdFR56SIdOmTcuUDImLi+PgwYNEREQwdOjQfAlOFD02NjaEhoayZ88eEhIScHd355VXXmH06NGFHZoQQgghhBBCPNMSEhIwGo2Z1v3Iipubm9VzjUYD/G9Ti5iYGAC6d++e5fkZozkydgvNyTUBdu7cia+vLx07dsxR/aIqT8mQbt26ZVk+YMAARowYQXh4+BMFJYouJycnli9fXthhCCH+y2QyPZPDEoUQQgghRGaurq6oVCoiIyOfuC13d3cAFi5cmO0yFhkjUHJ6zZUrVzJy5Ej69OnD119//cxucpGnZEh2unXrxieffMK7776b300LIcRzLz4+noMHD3Lq1Clu3LhBWloaDo6OVPD3p27dujRv3hw7O7vCDlMIIUQRYDKZiIyMJDIykrS0NDQaDV5eXrK2nxBFmJ2dHfXr12fv3r28//77j50qk50mTZpgY2PDrVu3aNeu3SPr+fv7U7ZsWbZs2cKbb75pGWHyKCVLlmTDhg28+eabloRI+fLl8xxnYcn3ZIjBYCAhISG/mxXPiP379xMZGUmfPn1yfa7RaGT16tUcPnzYsq3uCy+8wLvvvkvDhg2fQrRCPDvi4+PZvHkze/ftw6DXg0IJju4o3DxI0ady/vzfnD9/nnXr1hEcHEyNGjWoWrVqYYcthBCigOl0On777TeOHTvGX3/9RXJycqY6arUarVZLqVKl8PPzw8HBoRAiFUI8yvjx4+nduzc9evRg8ODBlC1blrt373Lw4EGmTJmS43ZKly7NiBEjmD9/PqGhoTRr1gwXFxdiYmI4d+4c9vb2jBgxAoBJkyYxdOhQevToQf/+/SlRogTh4eEcPXqUuXPnZmrbx8eHdevWMXDgQN544w1Wr17NCy+8kG+vQUHIt2SIXq/n0qVLLFq0yLKriHj+7N+/n/Pnz+cpGZKWlsby5cvp0qULb731FjY2Nmzbto0333yTpUuX0rJly6cQsRBFW2pqKjt37mTbtm3353/aOYOTD+jTQJeCWZdyv6LaFkxGTCYDx44d49ixY/zxxx+EhIQ8k5l6IYQQOWc2m7l27Rr79+/nyJEjpKT8t29QKEGpAoUCUABmMJvRGwycP3+e8+fPs2/fPgIDA2nRogXBwcE5WjxRCPF0ValShc2bN7Nw4ULmzp1LcnIy3t7eBAcHP3bUxsOGDBlCxYoV+eabb9izZw/p6el4e3tTo0YNqx1CmzZtyvr16/nyyy/57LPP0Ol0+Pn5ZVqk9UEeHh6sXbuWwYMHExISwsqVK6lZs2ae77ug5SkZUqVKlUfuJuPi4sKqVaueKCjxfLKzs+PAgQO4urpaypo0acKNGzdYvXq1JEPEcyU+Pp59+/axe/duEhMTUWgcUFRsgDn8MsSFATBz5kwAxo4dm2Ubf/zxB3/88Qf16tWjU6dO1KpVS9YXEUKIYsJkMnHjxg1+++03/vOf/xAaGnr/gMYB1Hb3k+ZmEzNnZN9XoFRx+vRpTp8+ja2tLQ0aNKBevXrUrl070+KMQoiCU7FiRRYsWJDlsW7dumW5jmdQUBCXLl3KVN6mTRvatGnz2GsGBATw1VdfPfL48OHDGT58uFWZs7Mz33333WPbLorylAx55513MiVDNBoNpUuXplmzZjg5OeVLcKJounLlCrNmzeLs2bOkpaXh5+dH9+7duXbtGtu2bQP+t61T165dmTFjBuPGjeP8+fOMGTOG2bNnc/PmTSpXrsykSZMICAgAQKVSWSVCABQKBVWqVOGPP/4o0HsUoqAZjUbCwsL4559/+P333/nz9GlMRiMKtS1K/3ooSlfHdOsspMTnrmFbR37//Xd+//133N3dCQgIoEKFCnh6eqLRaDCbzej1egwGA2azGZVKhZ2dHS4uLri7u+Pu7o6NTb7PqBRCCPEAvV5PSkoKKSkppKamkpaWRnp6Ounp6ej1etLT00lNTSUxMZG7d+9y584d/v333/+NAFGqUHhXQOFXGfO9SMy3zuT84iYjilLVwMYWXdS/HD16lKNHjwL3h8GXLVsWPz8/XF1dcXJyws7ODo1Gg1qtRq1Wo9FosLW1xd7eHgcHBxwdHXP9zbUQQhSGPL3DfTgbJJ4vQ4cOxdPTk88//xwnJydu3bpFREQEw4YNIzY2ln///Zc5c+YAWC34Ex0dzZQpUxg+fDguLi589dVXvPXWW/z00094enpmeS2TycTp06epWLFigdybEAXFbDazZs0adu7cmXUFZy8ULr6Yk2IwR1+H6Ou5T4QAqO1Q1WiLKfwicTE3+eWXX/jll1/yHHfZsmUZO3bsM7tquBBCFIb4+Hi++OILzp49m08tKsDBFYVfaRQeZVB4lEJho8EUdwfz7fO5bs0cfhllzRdRlq8DKfGY74ZivhdBVHw0UVG/51PM9/uQMWPGUKZMmXxrUwgh8uqJv+67fv068fHxuLu7y7z050BsbCyhoaF89NFHlvljwcHBluMeHh7cuXPHMtrjQfHx8cyfP9+yGGr9+vVp3rw5a9euZdSoUVleb926dVy/fp2pU6fm/80IUYjS09PZs2dPlscU3hVQlqmB8cZpuPfk26opnL1Q2jfA7FYSc/glzP+dZpMXt27d4uzZs5IMEUKIXLh69Wr+JEJsbFE4e6FwLwnO3iicvVHYqC2HzbfPgcmQ+3ZNBsy3z6F0L3l/cW5Hd8xpFTAnxmBOiMIcHw7JcXlr+wG3bt3ir7/+kmSIEKJIyHMy5Mcff2TWrFlERERYyvz8/Bg7dizt27fPl+BE0ePu7k6pUqX44osvuHfvHg0bNsTPzy9H5zo7O1vtCuPi4kJwcDBnzpzJsv6pU6eYPXs2AwYMoH79+vkRvhBFhq2tLfPnz+fmzZvExcURERHBjRs3uHLlCunR/2KM/hdsncDeFVT//VWtSwF9aq6uo3D2xnjxCOaofy1vYhUKBS4urtjaajCZzRj0evR6/X+nydhgb39/moybmxseHh54eHjg7u6Oo6MjTk5Oz9TCWEIIURTUrVuX6dOnEx8fj9lsRqfTkZqaSnJystUjY5qMTqezTJPJeOh0OswGHea4sP8ltRVKFK6+KLzK3Z8mU7om5qRY0GXeQSZbto4oStXAFBuGOeYG5thQSEvKVE2hUGBrZ4fmv1NkMqbJ2NnZYWdnZ5kmk9FfODg44ODggJ2dHQqFAgcHB2rUqJEPr6gQQjy5PCVDDh8+zKhRo6hUqRIffPABPj4+REZGsnPnTkaNGoW9vT3NmzfP71hFEaBQKFi5ciXz589n6tSppKSkUL16dcaPH//YhEVWe2R7enpy48aNTOUXL15k2LBhtGnThjFjxuRX+EIUKWXKlMn07Zher+fs2bMcPnyYY8eOYdAZUNg5oyhfBzxKYzqzJ+fTZVRqzOEXgfv7wTdv3pw6depQvnx51Gr1Y04WQgiRXxQKxRNvd24ymUhOTrasGXLt2jXOnz/P5cuXMcWHw7WT96fM+NfDfOuvnPcVds7gUQrTxUOQfj/h7uTsTM3Ahmi1WsqWLYuvry/u7u7Y29s/chMFIYR41uQpGbJ06VIaN27MihUrrHYmGDhwIAMHDmTp0qWSDCnGKlSowMKFC9Hr9Zw+fZovvviCt99+myNHjmR7XmxsbKayu3fv4u3tbVV269YtBg4cSLVq1Zg1a5Z0uuK5olarqVu3LnXr1qV///5s376dH3/8Ef3Fw+DocT8pkhCN+V44Yyd+8t+T7P/XgNkEZjMY08Gop1q1anTv3p3AwED5vySEEM8wpVKJs7Mzzs7OlC9fnkaNGgH3pyEfP36cX375hcuXL8PdW2BjC44eYDIwduJkQJG5r1CqwGSEtES4cxEXFxeatGlJ48aNqVKlCiqVqnBuVAghCkiekiEXL17kiy++yLRFo0Kh4PXXX2f06NH5Epwo2tRqNQ0aNGDw4MEMHTqUqKgo1Go1Op0uy/qJiYkcP37cMlUmMTGREydO8MYbb1jqREdHM2DAALy8vFiyZImsRi6eax4eHgwYMICXX36Z7777joMHD2L+5yA4uqP0rYzC1RfUtpCeivleJKbo65B0F7i/SF1ISAj16tWTJIgQQhRjbm5udOjQgQ4dOnDz5k0OHDjA4cNHuHfvgS+hNA5gowajAdJT7ifNAZWNDfWCgmjdujV16tSR3cOEEM+VPP3GUyqV6PX6LI8ZDAZ5412MXbx4kZkzZ9KxY0fKlClDUlISy5cvp1SpUpQtW5aKFSuyZcsWdu/eTbly5XB3d6d06dLA/c56woQJjBgxAmdnZ8se1v369QMgLS2NgQMHcvfuXcaNG8fVq1etrp3VoqxCPA+8vLwYPnw4Xbp0YfPmzfznP//B+O+pTPVUKhVBjRrRtm1bTCYTVatWld/HQgjxHClXrhwDBgygX79+XLhwgb/++osrV64QGRlJamoqtrZOeHmVx9/fnxdeeAG1Wk3t2rVxcHAo7NCFEKLA5SkZUrNmTVauXEnz5s2xs7OzlKenp7N69Wpq166dbwGKosXb2xsvLy+WL19OZGQkzs7O1KtXj9mzZ6NSqejevTtnz57l008/JT4+nq5duzJjxgzLuaNHj2bWrFncunWLypUrs2rVKry8vACIiYnh4sX76xu88847ma596dKlgrtRIYqgMmXKMHLkSAYOHMjp06e5ceMGKSkpODk5UaFCBWrVqoWTkxMpKSlcuHChsMMVQghRSFQqFTVq1Mh2sVLpK4QQz7s8JUOGDx9O//79adOmDe3bt8fLy4vo6Gh++ukn4uPjWbt2bX7HKYoIT09PZs+e/cjjTk5OfPHFF4883qJFC1q0aJHlsdKlS0vCQ4gccHZ2plmzZjRr1qywQxFCCCGEKHYmTJhATExMoVzby8uLzz//vFCunVtDhgwhJSWFdevWAbB161bGjx/P8ePHs9w8o6jJUzKkXr16rFmzhjlz5rBhwwbMZjNKpZJatWrxxRdfUKdOnfyOUwghhBBCCCGEeOpiYmKIiozERVWw040TjOYCvV5+a9GiBRs3bsTFxaWwQ8mRXCdDdDod27dvp169emzcuJHU1FQSEhJwcXHB3t7+8Q0I8Zz67rvviImJeeKt9YQQQhRf0lcIIUTR4KJSMKRkwX6oX34noUCvl988PDyeiREhGZSPr2LN1taWzz77jLt37+9YYG9vj6+vryRCRLZmzJjB7t27CzuMQnXixAmZmyuEECJb0lcIIYTIi3HjxtGpUyeOHj1K586dqVWrFq+//jqhoaHEx8fz/vvvU6dOHdq0acMPP/xgde6hQ4d47bXXqFWrFsHBwUyePJmUlBSrOteuXeONN96gZs2atGnThu3bt2eKYevWrWi1WmJj/7eb1Zw5c+jcuTOBgYE0bdqUUaNGERUV9VReg9zK0zSZMmXKFNocKiGEEEIIIYQQQliLjo5mzpw5DB06FBsbGz777DPGjBmDg4MDdevW5bXXXuP7779nzJgx1K5dm1KlSrF3715GjhxJt27dGD58ONHR0cydO5eEhATmzZsH3J8dMmDAAOzt7Zk1axYA8+fPJzk5GX9//2xjunv3LkOGDMHHx4fY2FjWrFlDSEgIe/bsKfTtvPN09b59+7JixQqaNWuGk5NTfsckHhISEkJMTAw7duxAo9FYHRsxYgSnT5/mk08+YdiwYZZyBwcHypcvT58+fXj11Vct22tqtVpLHbVajZubG1qtlvbt29OlSxfUarXl+MmTJ+nbt69Vm6VLl6Z79+688cYbqFSqp3XLQgghhBBCCCFy4d69e3z77bdUrFgRgKioKD799FMGDRpk2a2zZs2a/Pzzz+zfv5++ffsya9YsOnbsaLVoq5eXF0OGDGHYsGFUrlyZrVu3EhUVxY8//kj58uWB+58rO3bs+NhkyPTp0y0/G41GAgMDadasGSdOnKBJkyb5/ArkTp6SIVevXiUuLo5WrVoRHByMt7e35cN2hokTJ+ZLgAKmTp3Kyy+/zMqVK60SHkePHmXfvn0sXLjQkpSaPn06FSpUICEhgc2bNzNhwgT0ej29e/e2nBcSEkKnTp0wGAxERUVx9OhRPvnkEzZt2sTq1aszJbgy2kxMTGTnzp1MmzYNnU7H4MGDC+YFEEIIIYQQQgiRLR8fH0siBLAkLho1amQpc3FxwcPDg4iICK5fv05YWBgfffQRBoPBUqd+/fooFArOnz9P5cqVOXv2LJUrV7a0B1ChQgUqV6782JgOHz7M0qVLuXLlCklJSZbyGzduPJvJkPXr11t+/umnnzIdVygUkgzJR/7+/gwZMoRly5bRqVMnypYti06nY+rUqbRs2ZJ27dpx8uRJACpXrkzNmjUBaNy4MR07dmTDhg1WyZASJUoQEBBged6xY0c6dOjAkCFDmDFjBp999pnV9R9ss0mTJvz9999s2bJFkiFCCCGEEEIIUUQ8vItLxqh/Z2dnq3KNRoNOpyMuLg7AMmrkYeHh4cD9ESaenp6Zjnt5eVklUR529uxZhg0bRuvWrRk0aBCenp4oFAp69OiBTqfL+Y09JXlKhly8eDG/4xCPMXjwYHbv3s2UKVNYtWoVy5YtIyYmhrVr1z7yHJVKRZUqVTh8+PBj22/WrBkvvvgi27dvZ9y4cY+c/qRQKHjhhRc4ePCgVfmcOXM4fPgwt2/fxsnJifr16zNu3Dh8fHwsdUJCQnBwcKBDhw4sXryYqKgoateuzZQpU6hQoYKl3urVq9mzZw83btxAo9FQq1Ytxo0b99ghWEIIIYQQQgghcsbNzQ2ASZMmUatWrUzHMz7L+fj48Pfff2c6HhMTY2kjK/v378fJyYn58+ejVN7fuyUsLOzJA88nud5NRhQOjUbD1KlT+fXXX1myZAkrV67kvffeo2TJktmed/v2bauERHaaNGmCXq/nn3/+ybZeeHg4ZcuWtSrLWBhn+fLlTJgwgbCwMEJCQjJlCv/++2+WL1/OBx98wMyZM4mKimLgwIGkp6db6kRERPDGG2+wZMkSPvvsM0wmE7169SI+Pj5H9yGEEEIIIYQQInsVKlTAz8+P0NBQatasmenh6+sL3F9n5MqVK9y4ccNy7r///suVK1eybT8tLQ21Wm21pMauXbueyr3kRZ5GhlSpUiXTGiEZFAoFzs7O1KhRg0GDBhEcHPxEAYr/adCgAV27dmXBggVUr16dkJCQTHVMJhMGg4HExEQ2btzI+fPnGTJkSI7a9/PzA8i0U1BGm0lJSWzfvp2//vqL+fPnW9XJ6cI4d+/eZf369Zb5ZlWrVqVDhw5s27aNnj17AvDRRx9ZtdW4cWMaNmzIvn37LHWEEEIIIYQQQuSdQqFg3LhxjB49mpSUFFq0aIG9vT137tzh8OHDjBw5En9/f7p168bSpUt5++23ef/99zGbzSxYsAAvL69s22/cuDFr167l008/pW3btpw+fZodO3YU0N09Xp6SIe+88w7bt28nOTmZli1b4uXlRXR0NIcOHcLR0ZFWrVpx/PhxBgwYwFdffUXjxo3zO+7n1uDBg9m6dStvvvlmlru59OjRw/KzjY0NvXr1euQcsIeZzeYsyx9sMyOG9u3bW5XldGGchxfeKV++PJUrV+bMmTOWRMeZM2dYsGAB//zzj9VokAczkUIIIYQQQgjxtCQYzSy/k1Dg17Qr0CtChw4dcHFxYdmyZZZRG6VKlaJp06aWZIednR2rV6/mk08+YfTo0fj6+jJs2DB++uknUlJSHtl28+bNGT16NOvXr2fr1q3UqVOH5cuX065duwK5t8fJUzLE1dUVb29vdu3ahYODg6U8OTmZAQMG4Ovry/bt23nzzTdZtmyZJEPyUcYiOA9ugfugmTNnUrFiRZycnChVqlSmrXizExkZCYC3t3eWbcbGxrJixQq++uor6tevT7NmzYDcLYyT1cI7np6eREdHA3Dnzh0GDBhAjRo1mDJlCj4+PqjVaoYMGVIkFtkRQgghhBBCFG+PG/HwtNg9wbVnzJiRqSwoKIhLly5lKn94/cfGjRs/9jN75cqV2bBhg1XZq6++avW8W7dudOvWzaps0KBBDBo0yKosq5gKQ56SIevWrWPMmDFWiRAAR0dHBgwYwMyZMxk4cCC9e/eWXWUKWMWKFS07v+TW0aNH0Wg0VK9e/ZFt1qtXjw4dOjBz5kyaNm2KQqHI1cI4d+/ezbIs45pHjx4lJSWFxYsXW1ZDNhgM3Lt3L0/3JIQQQgghhBC58fnnnxd2CKIA5GkB1YiICGxsss6jqFQqy5oT3t7e2W61I4qOI0eO8PPPP9O1a9dMSa4HOTo6Mnz4cK5evcr+/fuB3C2M8/DCOzdu3ODKlSvUrl3b0pZCobD69/Xjjz/KvyMhhBBCCCGEEPkmTyND/P39+eabb2jWrJnVh1aDwcA333xj2QI1OjoaDw+P/IlU5Jvw8HDOnDmD0WgkOjqaI0eOsGPHDmrXrs3YsWMfe36XLl1YunQpX331FW3bts3Vwjienp4MHTqU9957z7Lwjq+vL127dgWwLLg7fvx4evXqxdWrV1m9enWmPbOFEEIIIYQQQoi8ylMyZMSIEYwYMYIXX3yR1q1b4+XlRUxMDAcOHCAyMpKFCxcC8OuvvxIQEJCf8Yp8sG7dOtatW4darcbNzQ2tVsuUKVPo0qXLI0f8PEitVjN06FAmTpzIyZMnc7UwTvXq1XnxxReZNWsW0dHR1K5dmylTpmBrawuAVqtl+vTpLF68mCFDhlC1alUWLFjA+++/n98vgxBCCCGEEEKI51SekiFt2rRh2bJlLFy4kPXr12M2m1EoFJZFL5s2bQrIXKunoXTp0lkuOPOoxXEelpvFarJr87XXXuO1116zPM/NwjivvvpqpsV2HtSlSxe6dOliVfbwIj/PouDg4EzbFgshhBAPkr5CCCGEKBh5SoYANGvWjGbNmpGamkpCQgIuLi7Y29vnZ2xCFCu9e/fmwoULhR2GEEKIIkz6CiGEEKJg5DkZksHe3l6SIELkwHfffUdMTAxVq1Yt7FCEEEIUUdJXCCGEEAXjiZMhQuTUunXrCjuEQnXixAn0en1hhyGEEKIIk75CCCGEKBh52lpXCCGEEEIIIYQQ4lklI0OKmcOHD/PNN99w/vx5kpOT8fb2pnnz5gwYMICyZcuSmprKqlWr+OGHHwgLC8POzo7SpUvTpEkTRo4cCUBCQgJr166lQ4cOVKpUqZDvSAghhBBCCCEKzoQJEwptMWsvLy/ZiKSASDKkGJk3bx7Lli2jbdu2TJkyBU9PT8LCwti2bRv9+/fn4MGDvPvuu5w/f96ybW1CQgLnzp1j//79VsmQxYsXU7lyZUmGCCGEEEIIIZ4rMTExREZGgZ1TwV44Lalgr/eU3b59m23bttGjRw98fX2tylu3bs2CBQto3759ocUnyZBi4siRIyxbtowhQ4YwatQoS3n9+vXp0qULBw8e5MaNG/znP/9h5syZVlvXtmvXzuqc/GY0GjGZTKjV6qd2DSGEEEIIIYTIN3ZO2AT3LNBLGk5sLNDrPW1hYWEsXryYFi1aWCVDfHx82LhxI+XLly+84JA1Q4qN1atX4+XlxfDhw7M83qpVKxISEgDw9vbOdFypvP9PISNLB/Dee++h1WrRarXcvn0bgDlz5tC5c2cCAwNp2rQpo0aNIioqyqqtkJAQhgwZwrZt22jXrh01a9bkwoULaLVa9u3bl+na3bp147333rM8j4iIYPTo0QQFBVGrVi369OnD+fPn8/CqCCGEEEIIIYQoSjQaDQEBAbi5uRVqHJIMKQYMBgN//vknDRs2zHb0RYUKFXBwcGDGjBn88ssvJCcnZ6rj4+PD4sWLARg1ahQbN25k48aN+Pj4AHD37l2GDBnC8uXLmTBhAmFhYYSEhGAwGKzaOX/+PKtXr+a9995jxYoVlChRgsDAQPbs2WNV78aNG/z999906tQJgHv37vH6669z8eJFPv74YxYtWoS9vT39+vXj7t27T/Q6CSGEEEIIIURx9X//93+0bNmS2rVr069fP/766y+0Wi1bt2611Nm6dSudO3emZs2aNG3alHnz5ll9ltu6dStarZa///6bgQMHEhAQwIsvvsj27dszXe/QoUO89tpr1KpVi+DgYCZPnkxKSgoAJ0+epG/fvgB0797d8iU73P8CXqvVsnfvXqv2tm/fTpcuXahZsyZBQUEMGjSIsLAw4P5SDhMnTqRp06bUrFmT5s2bW5Z5yCuZJlMMxMfHo9PpKFGiRLb1nJyc+Pzzz5k4cSJvv/02KpWKKlWq0LZtW/r164eDgwMajYaqVasCUK5cOQICAqzamD59uuVno9FIYGAgzZo148SJEzRp0sRy7N69e2zZsgU/Pz9L2UsvvcTs2bNJSkrCyen+/Lvdu3fj7OxM8+bNAVi7di0JCQls2rQJT09PABo2bEjbtm1ZtWoVH374Yd5fKCGEEEIIIYQohg4cOMDkyZN57bXXaNeuHRcuXOCDDz6wqrNmzRpmz55Nv379GDduHNeuXWPevHkYjUZGjx5tVXfMmDH06NGDN998k40bNzJu3Dhq1KhhWVNy7969jBw5km7dujF8+HCio6OZO3cuCQkJzJs3j+rVqzNp0iSmTp3K9OnTqVChQrbxr1y5ktmzZ9O9e3dGjhyJXq/nxIkTxMbGUqpUKaZPn87Ro0f54IMPKFWqFNHR0Rw5cuSJXjNJhhQDZrMZAIVC8di6HTt2pHHjxvzyyy+cPHmSEydOMH/+fHbu3MmWLVtwcHDI9vzDhw+zdOlSrly5QlLS/xb4uXHjhlUyRKvVWiVCADp06MD06dPZv3+/Zc2SH3/8kbZt26LRaAD49ddfCQoKwtXV1ZKhVCqV1KtXj3Pnzj3+xRBCCCGEEEKI58zSpUsJDg7ms88+A6Bp06bodDrLqP+kpCQWLlzIwIEDLetFNm7cGJVKxaxZs3jrrbdwd3e3tNenTx/69OkDQO3atTl06BA//fQTlSpVwmw2M2vWLDp27Gi1842XlxdDhgxh2LBhVptxVK5cmZo1az4y9sTERBYvXkzPnj2ZOnWqpbxNmzaWn8+dO0enTp3o2rWrpeyll17K8+sFMk2mWHB3d8fW1pY7d+7kqL6rqytdunRh+vTpHDx4kGHDhvHvv/+yefPmbM87e/Ysw4YNw8fHh1mzZrFx40a+//57AHQ6nVXdjFEdD/Ly8iI4ONgyVebixYtcvXrVMkUGIC4ujv3791O9enWrx+7du4mIiMjR/QkhhBBCCCHE88JoNHLhwgVatWplVZ6xFiTA6dOnSUlJoX379hgMBssjODiYtLQ0rly5YnXug190Ozk5UaJECcvnsevXrxMWFkaHDh2s2qpfvz4KhSLX6z2ePn2a1NRUunfv/sg61apVY9u2baxatYrLly/nqv1HkZEhxYCNjQ1169bl+PHj6PX6XO3aolAoeOutt1iyZAnXrl3Ltu7+/ftxcnJi/vz5lgVXM+ZwZdVuVl566SUmT55MXFwce/bssSRIMri6utK0aVOrBVUzZIweEUIIIYQQQghxX2xsLAaDAQ8PD6vyB7+gjouLA7AaWfGg8PBwq+fOzs5Wz9VqNenp6VZtvfPOOzlq63Hi4+MBLOtUZuXjjz/G1dWVNWvWMGvWLEqUKMHgwYN5/fXXc3WtB0kypJh48803GTRoEF9++SXvv/9+puO//PIL9evXx8bGBjs7O6tjN27cAP63y0xGMuXh0R5paWmo1WqrRMeuXbtyFeeLL77IJ598wr59+/jhhx/o0KEDKpXKcrxRo0bs3LmTihUrPnbKjhBCCCGEEEI87zw8PLCxsSE2Ntaq/MENKFxdXQFYvHhxpuUMAEqXLp3j62XsAjNp0iRq1aqV6Xh2SY3s2ouKisoyNrifnJkwYQITJkzg0qVLfPPNN0yZMoXKlStTv379XF0vgyRDiolmzZrx9ttvs3TpUv79919eeuklPD09CQsLY+fOnVy/fp0FCxYwdOhQunbtSt26dXFwcODatWusWLECZ2dnS5bQ29sbFxcX9uzZQ+nSpdFoNGi1Who3bszatWv59NNPadu2LadPn2bHjh25ijNjsdQvv/ySqKioTPO8+vfvz65du3jjjTfo27cvJUuWJDY2lr/++gtfX1/69++fXy+ZEEIIIYQQQjzzVCoVVatW5cCBA/Tr189Svn//fsvPderUwd7enoiICNq2bftE16tQoQJ+fn6EhoZa1hXJyqO+ZH9YYGAg9vb2bNmyJcvkysO0Wi3jx49n8+bN/Pvvv5IMETBy5EgCAwNZt24dH3/8McnJyfj4+NCoUSPGjx+Pj48PPXv25Ndff2XTpk0kJyfj6+tLcHAwb7/9NqVKlQLuL1g6bdo05s2bR//+/UlPT+fAgQM0b96c0aNHs379erZu3UqdOnVYvnw57dq1y1WcL730Ej///DOlSpUiMDDQ6pi7uzsbN25k/vz5zJkzh/j4eDw9Paldu/YT/6cVQgghhBBCiBxJS8JwYmOBXxPXvI2OHzp0KMOGDWPixIm0b9+ef/75x/LFtVKpxNnZmREjRjB79mwiIiIICgpCqVQSGhrKgQMHWLRoEfb29jm6lkKhYNy4cYwePZqUlBRatGiBvb09d+7c4fDhw4wcORJ/f3/Kly+PSqViy5YtqFQqbGxsslxI1dnZmXfeeYc5c+ZgMplo06YNJpOJkydP8tJLL1GzZk169epF27ZtqVy5MiqViu3bt6NWq6lXr16eXi+QZEix06JFC1q0aPHI48OHD2f48OGPbadt27ZZJh8GDRrEoEGDrMouXbpk9XzdunXZtt2hQwc6dOjwyOPe3t5WqxIXF8HBwcTExBR2GEIIIYow6SuEEKLweXl5Fc6FXR3yfO3WrVvzySefsHz5cnbu3Ent2rWZPHkygwYNwsnJCYABAwbg6+vLmjVrWL9+PTY2NpQtW5YWLVrkat1JuP+ZzsXFhWXLllmWTihVqhRNmza13IOHhweTJk1i5cqV7Ny5E4PBkOmzY4ZBgwbh4eHB119/zbZt23B0dCQwMNCy7kmdOnXYvn07t2/fRqlU8sILL7Bs2TIqVqyYp9cLJBkiRIHp3bs3Fy5cKOwwhBBCFGHSVwghROF7Vr+Y7d27N71797Y837RpEwBVqlSxlL300kvZbknbrVs3unXrlql89+7dmcoaN25M48aNs42pV69e9OrVy6qsdOnSWSZFXn31VV599dUs2/nwww/58MMPs71WbkkyRAghhBBCCCGEeIbFx8ezePFigoODcXR05Ny5cyxbtozWrVvnanHU54kkQ4QQQgghhBBCiGeYjY0NoaGh7Nmzh4SEBNzd3XnllVcYPXp0YYdWZEkyRAghhBBCCCGEeIY5OTmxfPnywg7jmaIs7ABE/goJCaFDhw6kp6dnOjZixAiaNm3KgQMH0Gq1lkdgYCBdu3Zl8+bNmM1mS/0H6zz4qFatmqXOyZMnWbZs2SPjOXbsGG+99Rb169enVq1adOrUiRUrVmSK7+TJk2i1Ws6dO5epjeyOCSGEEEIIIYQQuSUjQ4qZqVOn8vLLL7Ny5UqGDRtmKT969Cj79u1j4cKFltWEp0+fToUKFUhISGDz5s1MmDABvV5vtehOSEgInTp1srqGQqGw/Hzq1ClWr17N22+/nSmWtWvXMm3aNFq0aMH06dNxcnLi5MmTfPnllxw+fJjVq1dja2ub3y9BkZWamsr+/fvZsGEDsbGx+dKml5cXNWrUoGfPnjneCksIIcSzJzU1lY0bN3L+/Pkn2m1G+g0hhBDiPkmGFDP+/v4MGTKEZcuW0alTJ8qWLYtOp2Pq1Km0bNmSdu3acfLkSQAqV65s2ee5cePGdOzYkQ0bNlglQ0qUKEFAQECu47h48SIzZ86kY8eOzJs3z1IeHBxMQEAAgwcPZtGiRc/NHLaEhAQmTZpEWFhYntuYOXMmAGPHjrWUxcfHc/XqVf744w+mTZuGs7PzE8cqhBCiaElISGDChAmEhobm+tyH+w7pN4QQQoj7ZJpMMTR48GBKlizJlClTAFi2bBkxMTFMmjTpkeeoVCqqVKnC7du3c3ydRYsWsXjxYlJSUixTaEJCQgD45ptvABg1alSm85o3b06DBg349ttv0el0ubm1Z5LRaGTLli1PlAh5nNDQUL777jvi4+OznCIlhBDi2bVr1648JUKyExoayurVqwkPD5d+QwghxHNJRoYUQxqNhqlTp9K3b1+WLFnCypUr+eCDDyhZsmS2592+fRsfHx+rMpPJhMFgsCpTKBSoVCpee+01IiIi2L17N2vXrgWwTME5deoUWq2WMmXKZHmtNm3acOrUKc6fP0/dunWzvZ7JZMrZjT9lkZGRJCYmkp6ejk6nQ6fTkZqaSkpKCsnJySQlJZGUlERCQgKJiYmWP5OTk63WYnlafvjhB3744QfLc6VSafVQKBSZ/sx4AJl+zvBgmUajoVGjRpQvXx6DwcCdO3dIT0/H0dERlUpl9VAqlZY/lUqlpQ2z2YydnR3u7u5P/TURQoiiwmg0Eh0djclkQq/Xk56eTlpaGqmpqSQnJ5OYmEhiYiJ3794lLCyMlJQUIiMjMRgMT60f/OWXX/jll1+A+32GjY1Npt/hCoUCW1tbgoOD8ff3R6PRoFarrR42NjaWczP6DDc3t6cSsxBCCJFfJBlSTDVo0ICuXbuyYMECqlevbhmx8aCMxENiYqJlHvKQIUOs6syZM4c5c+ZkanvdunX4+fnh5+eHUqnMNJUmMjISrVb7yPhKlSplqfegHj165OY2C8y5c+f4+OOP83Sui0pBqgn0Tz8fYsVkMj2VN9Dff/99vrQzevRomjRpki9tCSFEUTdnzhyOHz9e2GE8kslkynaEyLZt23LVnrOzM19++eWThiWEEEI8NZIMKcYGDx7M1q1befPNN1GpVJmOP5h4sLGxoVevXrzzzjtWdfr27cvLL79sVebo6Ph0Aub+3OaKFStalf39999Mnjz5qV0zJ/z8/ChdunSuphFlSDAWcBbkGVCiRAn8/f0LOwwhhCgwDRo0KNLJkPzWvHnzLN97CCGEEEWFJEOKMbVabfXnwzISD05OTpQqVQqNRpOpjp+fn2WR1dzw9fXlzp07jzyesX6Gr6+vVXnFihUzXS8lJSXX189v3t7eLF68OMtjer0+y2kyD06ViY2N5fz5809tjRSNRkPFihVxdnbGwcHB8rC3t7d62NnZYWtri0ajQaPRYGNjYxni/PAUlwen12QlJSWFCxcuULVqVRwcHJ7KfQkhRHHRsmVLWrZsmeUxs9mMTqcjMTGRyMhI/v77b1xdXUlLSyMxMZG4uDhOnDiRr/2hQqHAz88PLy8vPD098fT0xMPDA3d3d1xdXXFycrL0I3Z2dtjY5P4tY1Hov4UQQohHkWTIcyyrxEN+adCgATt27CA0NDTLdUMOHjyIo6MjNWrUeCrXL0hqtRo3N7fHzo++e/cuK1euJDIyMtPWujlZU2TatGkoFAqr68gWiUII8exTKBTY2dlhZ2eHo6MjOp0uU6J50KBBud5a12w2M23aNMxmMy4uLgB4enpSq1YtevXqJf2GEEKI55okQ8QTUavVWc4xDgkJYfv27cyfP5+5c+daHfvPf/7DiRMnGDRoELa2tgUVaqGzt7enTZs2MpJCCCFErtnb29O/f//CDkMIIYQoNiQZIrIVHh7OmTNnMpVXq1bNMjXDYDCwdu1aAgMDcXJyokKFClStWpWxY8cybdo0kpOTee2113BycuLUqVOsWrWKevXqMXz48IK/ISGEEEIIIYQQzz1JhohsrVu3jnXr1mUqP3DgAKVLl6Zly5a8/vrrrFixgrt371K/fn1L/X79+lGpUiVWr17NuHHjSEtLo2zZsgwdOpQ333wzyzVKhBBCCCGEEEKIp02SIcVY6dKluXTpUqbyoKCgLMsflpM6NjY2TJ48+ZG7vTRu3JjGjRs/tp3sYsppvEIIIYQQQgghRE4oCzsAIYQQQgghhBBCiIIkyRAhhBBCCCGEEEI8VyQZIoQQQgghhBBCiOeKJEOEEEIIIYQQQgjxXJFkiMiRcePG0alTpyyPTZ06lVatWhVwREIIIYQQQgghRN7IbjJCPKdSU1PZuHEj58+fJyYmJsfneXl5UaNGDTp37vwUoxNCCFGU5KTPyOgfevbsib29fQFHKIQQQuSOJEPEU5eWloadnV1hhyEekJCQwIQJEwgNDbWUzZw5E4CxY8dme258fDxXr17lt99+o3fv3k81TiGEEIUvqz4DMvcbGf3DH3/8wbRp01CpVAUeqxBCCJFTMk1G5KutW7ei1Wo5ffo0b775JgEBAZY3SxEREYwePZqgoCBq1apFnz59OH/+vNX527dvp3fv3jRo0ID69esTEhLC2bNnC+NWiqWkpCQuX77Ml19+melNbW6FhYVx7NgxzGZzPkUnhBCiKNq6dWuu+ozQ0FBWrVpFZGQkBoPhKUYmhBBC5J2MDBFPxejRo+nZsydDhgzBzs6Oe/fu8frrr+Pg4MDHH3+Ms7Mz69ato1+/fvz00094enoCcPv2bbp06ULZsmVJT09n9+7d9OnTh507d+Lv71/Id/X0mc1mTCYTBoPB8jAajZY/H3yYzWaMRiMmk8lSJz09nbS0NFJSUkhISODevXvcvXuXqKgoIiMjSUpKytd4jx07xrFjx1AplShVKmxsbPDz88Pb2xs7O7tMD1tbW8ufGQ87Ozs0Go3VQ61Wo1arUalUKBSKfI1ZCCGKg4w+4OH+IaMfyaBQKFAoFCiVSpRKpeV5xvl6vR6dTmfpN+Li4oiJiSEqKorw8HDCwsJITEzMdXyHDh3i0KFDAKhUKnx9fSlZsiRubm64urri6OiIo6OjpX94+Pe/Wq3GxsYGGxsby88Z/YKMOBFCCJEfJBkinorevXszcOBAy/OFCxeSkJDApk2bLImPhg0b0rZtW1atWsWHH34IwLvvvms5x2Qy0bhxY86dO8e2bdsYNWpUwd7EU3bkyBG++OKLArmWEvCwUeJnZ4O7WsnFFD2Jxvwb0WE0mTCaTOj1eq5fv87169fzre2HffHFF1SoUOGptS+EEEXJDz/8wIoVKwrt+h42SjQKSH+CLsNoNHLnzh3u3LmTf4E9pGPHjgwePPiptS+EEKL4kWSIeCqaN29u9fzXX38lKCgIV1dXy5BZpVJJvXr1OHfunKXetWvX+OKLLzh9+jR37961lN+4caNA4i5IGo2mwK5lAnRm0JnN6Exm3G2UJBqNT9yuElAA5v9e42nTaDTY2MivLSHE88PW1rZQr59uNmOnVJCeywS6DaBSgBEwmZ9+HyFrkwkhhMgt+VQhckSlUmF8xIdno9GY6QNqxuiPDHFxcZw5c4bq1atnOr9s2bLA/fUsBgwYgIeHB+PGjaNkyZLY2toyceJEdDpdPt1J0REcHMz27dsfedxsNltNkzEYDJYpMSaTyfLIGBqdMU0mNTWVlJQUEhMTiY+PJzY21jJN5nZ0NKG6J0+CZDABzs7OODk54ejoiIODAw4ODo+dIvPwVBlbW1uraTKS8BBCiPtat25N69ats63zcN/w4FpOWU2RyWqaTHJyMgkJCcTHxxMTE0NkZCQRERGEhYURGRmZ67gNgMEMNjY2+Hh54e3tjYeHB66urri4uODs7GzVX9ja2lpNkXlwakzGdBmZNimEECI/yScOkSMeHh6P3EovOjoaDw+PbM93dXWladOmvPfee5mOZYyQOHPmDBERESxfvpwqVapYjicmJuLn5/cE0T+bFAqF5Q1hftHr9URGRnLnzh1CQ0M5duwYkZGRpKWl8dFHH2E2mzO9YX5wvrlCocDd3Z2qVavyyiuvEBkZSbVq1XBwcMi3GIUQQuRORrIjt1QqFRqNBkdHx2z7cb1ez40bN9iyZQvXrl0jISEBs9nMxx9/jNlstvTjjo6OlClThtatW1OqVCmcnJy4ffu29BNCCCGKJEmGiBypX78+K1as4LfffqN+/fqW8sTERE6dOvXYLVYbNWrEzp07qVix4iPfEKWlpQFYffj/888/CQsLo3LlyvlwF0KtVlO6dGlKly5NgwYNePXVV/PcVkpKClFRUfkYnRBCiKJIrVZTuXJlxo0bl6vzUlJSZDSHEEKIIkuSISJHmjRpQr169Xj33Xd55513qFy5MlFRUaxcuRIbGxtCQkKyPb9///7s2rWLN954g759+1KyZEliY2P566+/8PX1pX///gQEBODg4MCUKVMYPHgwkZGRLF68GF9f3wK6SyGEEEIIIYQQzwNJhogcUSqVLF++nIULF7JmzRqioqJwcnIiODiYRYsW4ePjk+357u7ubNy4kfnz5zNnzhzi4+Px9PSkdu3atG3bFgAvLy8WLFjArFmzGDZsGOXLl+eTTz5h5cqVBXGLQgghhBBCCCGeE5IMETnm5OTERx99xEcfffTIOt26daNbt25ZHvP29ubzzz/P9hrNmjWjWbNmVmUP70wjhBBCCCGEEEI8idyvtiWEEEIIIYQQQgjxDJNkiBBCCCGEEEIIIZ4rMk1GiALy3XffERMTQ9WqVQs7FCGEEEWU9BVCCCFEwZBkiBAF5MSJE+j1+sIOQwghRBEmfYUQQghRMGSajBBCCCGEEEIIIZ4rMjJE5FlISAgxMTHs2LEDjUZjdWzEiBGcPn2aTz75hGHDhlnKHRwcKF++PH369OHVV19FoVAAoNVqLXXUajVubm5otVrat29Ply5dUKvVBXNTQgghhBBCCCGKPUmGiDybOnUqL7/8MitXrrRKeBw9epR9+/axcOFCnJycAJg+fToVKlQgISGBzZs3M2HCBPR6Pb1797acFxISQqdOnTAYDERFRXH06FE++eQTNm3axOrVqy1tCSGEEEIIIYQQT0KmyYg88/f3Z8iQISxbtoxbt24BoNPpmDp1Ki1btqRdu3aWupUrVyYgIIBmzZoxb948ypcvz4YNG6zaK1GiBAEBAdSrV4+OHTsyffp0li5dyrlz55gxY0aB3psQQgghhBBCiOJLkiHiiQwePJiSJUsyZcoUAJYtW0ZMTAyTJk165DkqlYoqVapw+/btx7bfrFkzXnzxRbZv305SUlK+xS2EEEIIIYQQ4vklyRDxRDQaDVOnTuXXX39lyZIlrFy5kvfee4+SJUtme97t27fx8fHJ0TWaNGmCXq/nn3/+yY+QhRBCCCGEEEI85yQZIp5YgwYN6Nq1KwsWLKBy5cqEhIRkqmMymTAYDMTFxbFs2TLOnz9P+/btc9S+n58fADExMfkatxBCCCGEEEKI55MsoCryxeDBg9m6dStvvvkmKpUq0/EePXpYfraxsaFXr1688847OWrbbDbnW5xCCCGEEEIIIYQkQ0S+yNj69lFb4M6cOZOKFSvi5OREqVKlMm3Fm53IyEgAvL29nzxQIYQQQgghhBDPPUmGiAJRsWJFatasmadzjx49ikajoXr16vkclRBCCCGEEEKI55GsGSKKtCNHjvDzzz/TtWtXHBwcCjscIYQQQgghhBDFgIwMEUVGeHg4Z86cwWg0Eh0dzZEjR9ixYwe1a9dm7NixhR2eEEIIIYQQQohiQpIhoshYt24d69atQ61W4+bmhlarZcqUKXTp0gUbG/mnKoQQQgghhBAif8gnTJEvSpcuzaVLlzKVBwUFZVn+sJzUedYFBwfL9sBCCCGyJX2FEEIIUTAkGSJEAenduzcXLlwo7DCEEEIUYdJXCCGEEAVDkiFCFJDvvvuOmJgYqlatWtihCCGEKKKkrxBCCCEKhiRDhCggJ06cQK/XF3YYQgghijDpK4QQQoiCIVvrCiGEEEIIIYQQ4rkiyZCnJCQkhA4dOpCenp7p2IgRI2jatCkHDhxAq9VaHoGBgXTt2pXNmzdjNpst9R+sU6NGDZo0acJbb73Fpk2bMn17dPLkSav6AQEBtG7dmpEjR/Lrr79mimXRokUEBgbm/wuQQ7dv30ar1bJ3795Ci0EIIYQQQgghxPNFpsk8JVOnTuXll19m5cqVDBs2zFJ+9OhR9u3bx8KFC3FycgJg+vTpVKhQgYSEBDZv3syECRPQ6/X07t3bcl5ISAidOnXCYDAQFRXF0aNH+eSTT9i0aROrV6+2tJUho02dTkdoaCh79uxhwIABvP7660yePNlS77XXXqN58+ZP+dV4NB8fHzZu3Ej58uULLQYhhBBCCCGEEM8XGRnylPj7+zNkyBCWLVvGrVu3ANDpdEydOpWWLVvSrl07S93KlSsTEBBAs2bNmDdvHuXLl2fDhg1W7ZUoUYKAgADq1atHx44dmT59OkuXLuXcuXPMmDEj0/Uz2gwKCqJ79+6sWbOGIUOG8O2337Jz505LPT8/P2rVqvWUXoXH02g0BAQE4ObmVmgxCCGEEEIIIYR4vkgy5CkaPHgwJUuWZMqUKQAsW7aMmJgYJk2a9MhzVCoVVapU4fbt249tv1mzZrz44ots376dpKSkx9YfMWIE3t7efPvtt5ayh6fJpKSkMHXqVNq1a0ft2rVp1aoVkyZNIjEx0aotrVbLqlWrrMpWrVqFVqu1PP/888+pX78+ERERlrIzZ85QrVo1S7LnUdNktm/fTpcuXahZsyZBQUEMGjSIsLCwx96jEEIIIYQQQgjxOJIMeYo0Gg1Tp07l119/ZcmSJaxcuZL33nuPkiVLZnve7du38fHxydE1mjRpgl6v559//nlsXRsbG4KDgzl//vwjV6pPS0vDaDQycuRIvvrqK9577z1+++033nnnnRzF86DRo0fj7e3NuHHjMJvNpKSk8OGHH9KwYUP69OnzyPNWrlzJ2LFjqV69OosXL+bzzz+nXLlyxMbG5joGIYQQQgghhBDiYbJmyFPWoEEDunbtyoIFC6hevTohISGZ6phMJgwGA4mJiWzcuJHz588zZMiQHLXv5+cHQExMTI7qlyhRAr1ez7179/Dy8sp03MPDwzKSBcBgMFC6dGlef/11rl+/jr+/f46uA2Bra8usWbPo1asX69at49q1a9y7d49169Y98pzExEQWL15Mz549mTp1qqW8TZs2Ob6uEEIIIYQQQgiRHUmGFIDBgwezdetW3nzzTVQqVabjPXr0sPxsY2NDr169cjwS48FdZ3JTX6FQPLLO9u3b+frrr7l58yYpKSmW8hs3buQqGQJQo0YNhg4dyqxZs9Dr9cybNw9fX99H1j99+jSpqal07949V9cRQgghhBBCCCFySpIhBUCtVlv9+bCZM2dSsWJFnJycKFWqFBqNJsdtR0ZGAuDt7Z2j+hEREajValxdXbM8/vPPPzN27Fh69uzJyJEjcXNzIzo6mnfeeQedTpfjuB700ksvsWjRInx8fGjbtm22dePj4wFyPE1ICCGEEEIIIYTILVkzpAioWLEiNWvWxN/fP1eJELi/Va9Go6F69eqPrWswGDhx4gQ1a9bExibrPNjevXupWrUqU6dOpXnz5tSuXRsXF5dM9TQaTaZ1R+7du5epnslkYsKECfj7+5OQkMCSJUuyjTFjV5moqKjH3o8QQgghhBBCCJEXkgx5hh05coSff/6Zrl274uDg8Nj6CxcuJDo6mjfeeOORddLS0jKNYNm1a1emen5+fly7ds2q7NixY5nqrVq1ir/++ov58+fzwQcfsGLFCs6ePfvI6wcGBmJvb8+WLVsedztCCCGEEEIIIUSeyDSZZ0R4eDhnzpzBaDQSHR3NkSNH2LFjB7Vr12bs2LGZ6l+5cgWj0Uh6ejqhoaHs3r2bY8eOERISwksvvfTI6zRq1IipU6eyePFi6tSpw5EjRzh+/Himeu3atWPt2rXUqlWL8uXLs3379kyLuF66dIkFCxYwYsQItFotL7zwAgcPHmTMmDHs2LEDOzu7TO06OzvzzjvvMGfOHEwmE23atMFkMnHy5EleeuklatasmYdXTwghhBBCCCGE+B9Jhjwj1q1bx7p161Cr1bi5uaHVapkyZQpdunTJcsrL+PHjAbCzs8PT05PatWuzZs0aGjVqlO11evXqxe3bt9mwYQOrV6+mSZMmzJ0712qRV4Bhw4Zx9+5dFi9ejFKppEePHlSpUoU5c+YAkJ6ezpgxY6hRowYDBw4E7i/aOmPGDDp37szs2bP5+OOPs4xh0KBBeHh48PXXX7Nt2zYcHR0JDAzE09Mz16+bEEIIIYQQQgjxMEmGFIDSpUtz6dKlTOVBQUFZlj8sJ3Vy22aG4cOHM3z4cMtzlUrF2LFjM402ebhNBwcHpk+fnqm9QYMGAffXFNm5c2em435+fvz222+W5496bV599VVeffXVHN/HsyA4ODjHWyALIYR4PklfIYQQQhQMSYYIUUB69+7NhQsXCjsMIYQQRZj0FUIIIUTBkAVUhXgOpKWlcfHiRRISEgo7FCGEEEVUZGQkoaGhhR2GEEIIUSBkZIgQxdzff//NrJkzuZeQgEajYciQIbRu3bqwwxJCCFGEHDhwgMWLF2M2m+nWrRt9+/Yt7JCEEEKIp0pGhghRjN28eZNPP/2UpMRE6jhpUBn1LFq0KMttkIUQQjyf7ty5w7KlS7FTgoeNkq1bt/LHH38UdlhCCCHEUyXJkCLg8OHDDBo0iODgYKpXr06jRo14++23OXToEGazGYBx48bRqVOnXLe9detWdu3ald8h5+i6Wq2W2NjYAr+2uC8tLY1Zs2aRlpZGZw972rjb08vLEY1SwcKFCwkPDy/sEIUQQhQBX3/9NXqDgXZu9nTxckAJrF61CqPRWNihCSGEEE+NJEMK2RdffMHgwYOxtbVl0qRJfP3113z88cc4OjoydOhQDh8+/ETtb9u2jd27d+dTtDnXokULNm7ciIuLS4FfW9y3atUqwsLCqO+s4QUHNQDeGhXt3e1IS0tjzpw5pKenF3KUQgghCtNff/3FqVOnUCvgp7hUfribipdaSdidO+zfv7+wwxNCCCGeGlkzpBAdOnSI5cuX8+6771ptbwvQoUMH+vXrh1L5bOarPDw88PDwKOwwnls///wzP//8M74aFc1c7ag56mMAzn3xKVUcNNxMM/LXtWssWbKEESNGPLP/zoQQQuRdfHw8n3/2GQB6M3w2YwYAY8eORQGsX7+eRo0a4ezsXIhRCiGEEE+HfAIqRGvWrMHb25uhQ4dmebxWrVrUqFEjy2OLFi0iMDAwU3lgYCCLFi0CICQkhFOnTnHo0CG0Wi1ardZy7NChQ7z55ps0bNiQOnXq8Nprr3HkyBGrtjKmupw9e5Z+/fpRu3Zt2rVrx9GjRzGZTMyfP5/GjRvTsGFD5s6di8lkynRuxjSZ27dvo9Vq2bFjB1OnTqV+/fo0adKEmTNnYjAYrK577do1hg4dSt26dQkICGDw4MHcunUrh69q8WQ0GjO9TlkxmUzs3r2bJUuWYK9S0sXTAZVCkalea3c7SmlUHDp0iHnz5pGUlPQ0whZCCFGIjEajZbrtw8xmM9OmTSNdr8/6OJCYmMiCBQvQP6KOEEII8SyTkSGFxGAw8Oeff9KuXTtsbJ7OX8PkyZMZM2YMdnZ2jB07FgA/Pz/gfnKiZcuWDBgwAKVSyZEjRxg8eDBr164lKCjIqp1x48bRu3dvBg4cyIoVKxgxYgTdunUjKSmJGTNm8Ndff7Fo0SJeeOEFOnfunG1M8+fPp3Xr1syfP58///yTxYsXU7ZsWXr37g1AaGgovXr1onLlysyYMQOFQsGyZcvo378/e/fuRaPRPIVXquiIjIzk22+/JSIiApPJRFpaGndjYkhJTQVAo9Hg5eWFo6MjCoUCxQOJDpPJxN27d4mNjcVRpaS7lwOuNlnnO20UCl71dmRrdDJHjx7lt99+IyAggLfeegtvb+8CuVchhBD5Jzw83NJ/pKenExUZSWpaGmq1DT4+vtjb26NSqVAoFJhMJmJjY4mJicm2TY0Cfv/9d8aPG8fMWbNQqVQFdDdCCCHE0yfJkEISHx9Peno6JUqUsCo3m81WC5Yplco8T2GoVKkSTk5OODg4EBAQYHXsjTfesPxsMpkICgri6tWrfP/995mSISEhIZZkha+vL507d+bcuXN8//33ADRt2pSDBw+yd+/exyZDatWqxcSJEwFo3Lgxx44dY9++fZb2Fy9ejIuLC2vWrMHW1haAOnXq0Lp1azZt2kSfPn3y9Fo8K3bt2pXtOjHp6encuXPnse1087LHV5P9m1Y7pYLu3o4sD08kNS2NEydOUK1aNV5++eVcxy2EEKJwbdmyhaNHj2Yq1+sNhIWF5alNVxsl0XoTV69dIzU1FScnpycNUwghhCgyJBlSSDKGrSoemsKwb98+3nvvPcvzPn36MGnSpHy/fkREBPPmzePYsWNER0db4qlevXqmuo0aNbL8XL58eQAaNmxoVcff35/r168/9rpNmjSxel6pUiV+++03y/Nff/2Vjh07olKpLNNCXFxc0Gq1nD9/Pmc39wzr0qULBoOBqKgozGYzSUlJREdFkZiUhNlsxsHBAS8vL9zc3FAqlVb/fgwGAzExMYSFhbE5OoXuXg6UsH30f/EUo4nNMSmkmsw4OTlRr169TH8/Qgghng2vvfYaCoWC6OhoUlNTCQ8PJzk5GTtbW3z9/HB2draMDDGbzcTExDx2Cmq66f57gypVquDg4FAQtyGEEEIUGEmGFBJ3d3c0Gg0RERFW5Q0bNmTz5s0Aj1xL5EmZTCaGDh1KYmIiI0aMoFy5ctjb2z9yu9UHF07LmKby8C4xarU6RzuTPLwI28PnxcXFsXbtWtauXZvpXDs7u8e2/6zz8vLi7bffzvP5ZrOZn376ieXLl7MpJoUQH0fc1ZlHiOhNZjbHpBCRbqR169YMHDgQe3v7JwldCCFEIfL19eWdd97JcX2z2czq1avZtWtXlscdlAruGc0EBwczevRoWWhbCCFEsSPJkEJiY2NDnTp1OH78OEaj0TIP19XVlZo1awJkuz6Gra1tpgXN0tPTSf3v2hLZuXnzJv/88w9ffvklbdq0sZSnpaXl5VbylaurK82bN+f111/PdMzR0bEQInq2KBQK2rVrh0ajYcGCBWy/m0KIrxPnvvjUqt7PcalEpBtp27Ytw4YNyzRCSQghRPGmUCjo378/Fy5c4OrVq3jYKJk6fhzOKiUBjmrOJ+txd3dn+PDhT21tMyGEEKIwSZq/EL355ptERUWxbNmyXJ/r6+uLXq+3GuJ67NixTKvGq9VqdDqdVVnGc7VabSkLCwvj9OnTuY4jvzVs2JArV65QrVo1atasafWoUKFCYYf3zGjZsiUdOnQgWm/icLx1kuvv5HTOp+h5oXJlBg8eLIkQIYR4TqlUKgYPHgyAvVLBsJLO9PVzwowCA/fXF5MvIoQQQhRXkuovRC1atGDw4MEsXLiQixcv0qFDB3x8fEhMTOT3338nOjr6kW9CmjVrhoODAxMnTmTQoEFERETwzTffWCU4ACpUqMD27ds5ePAg3t7e+Pj4UKFCBfz8/Czb4aamprJw4UJ8fHwK4razNWLECLp3785bb71Fjx498PLyIiYmhlOnTlGvXj06depU2CE+M/r378/ff//NH7duUdJWRVUHDZHpRn6KS8PBwYEPRo/O9O9FCCHE8+WFF16gUaNGHDt2jEupejxsVJxNTqdMmTK0aNGisMMTQgghnhoZGVLIPvjgA5YvX05aWhpTpkyhX79+TJgwgcuXLzNt2jRGjhyZ5Xnu7u4sXLiQ2NhY3nnnHTZv3szs2bMzfbgdNGgQderUYezYsXTv3p3vv/8ejUbDokWL0Gg0vPfeeyxYsIC3336bBg0aFMQtZ6tcuXJs2rQJNzc3pkyZwltvvcWcOXNITU1Fq9UWdnjPFFtbWz788EPs7e3ZE5vKj7EpbIxOxgC8//77+Pr6FnaIQgghioC+ffui0Wj4OS6NHXdTMAMDBw6UrXSFEEIUawrzw/MqhChmzp07B2BZi6WwpKSkcOHCBapWrVqgq/JfunSJmTNnEhsbi52dHcOGDaNZs2ZP3G5h3c/TUpzupzjdC8j9FHXF4X6KSj8Bhfd6HjlyhAUL5mM0mujZs6dly/snURz+bTxI7qdoK073U5zuBYrf/YjiQ6bJCFHMabVali9fzq1bt/Dz85P530IIITJp1qwZNWrUQKfTUaJEicIORwghhHjqJBkixHNArVZTsWLFwg5DCCFEEebh4VHYIQghhBAFRtYMEUIIIYQQQgghxHNFkiFCCCGEEEIIIYR4rhTLZMiiRYvQarU0bdoUk8mU6figQYPQarUMGTIEgJMnT6LVai0LqOVUXs97EuPGjctye9mrV68yduxYWrRoQY0aNahbty69evXi//7v/0hNTbXU27p1K1qtltjY2AKLWQghhBBCCCGEKEqK7ZoharWauLg4Tp48ScOGDS3lsbGxHDt2zGol4+rVq7Nx48Zcr6mQ1/Py24EDBxg5ciQVK1Zk2LBhlC9fntTUVE6cOMHs2bO5efMmY8eOLdQYhShMqampbNy4kfPnzxMTE2N1zMvLixo1atC5c+dCik4IIURByKovyOgDevbsib29fSFHKIQQoiAV62RIw4YN2b17t1Uy5Mcff8THx4dSpUpZypycnAgICMj1NfJ6Xn6Kjo5mzJgx1KtXj+XLl6NWqy3HmjdvzltvvcXp06cLMUIhCldCQgITJkwgNDTUUjZz5kwAxo4dS3x8PFevXuW3337Ll60khRBCFD2P6wv++OMPpk2bhrOzc2GFKIQQooAVy2kyGTp16sRPP/1Eenq6pWz37t107NgRhUJhKctquotWq+Wrr75i4cKFNGrUiKCgIMaPH09KSspjz1uxYgVz5swhODiYevXqMXPmTMxmM8ePH+eVV14hMDCQvn37Eh4ebhXvnDlz6Ny5M4GBgTRt2pRRo0YRFRWV7T1+//33JCcnM378eKtESAYvLy/atm37yPMfNdVnyJAhhISEWJVdu3aNd999lwYNGlC7dm1efvlldu/ebTmu0+mYMWMGTZs2tXzTvmvXLqs2rly5wqBBgwgKCqJ27dq0a9eOr776yqrO6dOn6du3LwEBAdStW5cPPviAu3fvZvs6iOeX2WwmPT2de/fuERERwfXr1/nnn3/4888/OXbsGIsXL7Z68/soYWFhbN++naNHj3L8+HFOnz7NxYsXuXXrFtHR0SQnJ2M0GgvgjoQQQuSV2WxGr9eTlJREbGwsERERhIaGsm7dumz7gtDQUDZs2EBERARxcXGkpKTI73whhCjmiu3IEIBWrVoxceJEjhw5Qps2bQgLC+P06dNMnjyZs2fPPvb8DRs2ULduXWbMmMH169eZPXs2np6ejB49+rHnBQcHM3v2bP766y8WLVqE0Wjk+PHjDB06FLVazWeffcaECRNYvXq15by7d+8yZMgQfHx8iI2NZc2aNYSEhLBnzx5sbLL+qzp16hS+vr5Urlw5dy9OLt24cYOePXtSokQJJkyYgLe3N5cvX+bOnTuWOqNHj+bw4cO8//77VK5cmR9++IHRo0djNBrp0qULAEOHDsXT05PPP/8cJycnbt26RUREhKWN06dPExISQvPmzZk3bx6pqanMnz+foUOH8v333z/VexT3vzn7z3/+g8lkwmw2Wx1TKBSWMoVCgU6nIzIykhs3bmBra5tlfZPJhMlkwmg0Wn5+8JFVudFotDwMBgMGgwG9Xo9eryc9Pd3ySEtLQ5eWRmpaWr69Yb169SpXr17Nto6dnR329vbY29tjZ2eHra0tGo0GjUaDWq1GrVajUqlQqVQolUrLIyMBq1Aosvw5o07G48FzM9pSqVRZ/p1ktJHxd5Cenm71d/OgkiVLEhgYaJUQFkKIvDAYDBw/fpz4+HirPiJDxu+imzdvolars6zzoIzf+xl/6vV6Sz+Qnp6eqR9IT09Hp9Oh0+nu9wn//Tm7a2Rn79697N2716pMo9FYfufb2tpiMpnw8PDAwcHBqg/I+P1vY2ODjY2N1e99INPv6QdfC7PZbHk8yoP9w8Pn50RW9fV6PTY2NlStWjXH7QghRHFSrJMhdnZ2tGnTht27d1v+rFixIlWqVMnR+V5eXsydOxeAZs2ace7cOfbt2/fYZIivr69l6GXTpk05ePAg33zzDXv27LGsLxIZGcmnn35KQkICLi4uAEyfPt3ShtFoJDAwkGbNmnHixAmaNGmS5bWioqIoUaJEpnKDwWD5WaFQZPoQlVuLFi1CrVbz3Xff4eTkBECjRo0sxy9evMhPP/3EpEmT6NOnj+Xeo6KiWLhwIV26dCE2NpbQ0FA++ugjWrVqBUBwcLDVdebOnUuNGjVYvHixpcOvXLkynTt35vDhwzRv3vyJ7kNk77PPPuPy5cuFHUaeOSgVOKkUOKmUOKoUOCqVnE9JJ8mYszeMLioFdZ1tSTaaSDaaSTKaSDSaSTSa0P+3ibS0NNLS0oiLi3uKd/J0ffTRRzRo0KCwwxBCPOO2bdvGhg0bCjuMR7JRgK1Cga1SQYLRhOExXYEKsFUqSDWZyaiakXS5d++epV5ORhs+S2rWrIm/v39hhyGEEAWuWE+TAejcuTO//PILycnJ7N69O1eLJDZu3NjqeaVKlaxGMTzKg2uUAPj7++Pj42O10Gr58uUBrNo7fPgwvXr1om7dulSrVo1mzZoB90dlPMqD3wpniI2NpXr16pbHK6+88tiYH+fEiRO0a9fOkgh52B9//AFAx44drcpfeuklwsLCCA8Px93dnVKlSvHFF1+wbdu2TK9lamoqf/75J+3bt7caFeDv74+3t3eB7trzvOrZs+czPV86xWQmSm/i3zQD55L1nEjU5TgRApBgNPNLfBqnEtP5O0XPTZ2RWMP/EiHFQdOmTeVbQCFEvmjSpEmR/n1iMEOyyUys4fGJEAAj9/uRYvQr/7EaNWqEt7d3YYchhBCFoliPDIH7v+QdHR1ZsmQJly9fZsmSJTk+N2PERga1Wm21/khuzsuqDO6vswFw9uxZhg0bRuvWrRk0aBCenp4oFAp69OhhqZMVX19fbt68men6mzdvBuDLL7/k9u3bj435ceLj4/Hx8Xnk8Xv37mFjY4O7u7tVuZeXl+V4iRIlWLlyJfPnz2fq1KmkpKRQvXp1xo8fT/369UlISMBoNDJ9+nSrUTIZHl5jReS/unXrsm7duhzVTUlJ4cKFC1StWtVqd6anwWg0WoZI63Q6y9Do1NRU0tLSSE1NzfTIOJ6YmMjff/9NWlpattewtbWlZMmSeHt74+joaJkGY29vb3nu4OCAg4OD5VjGEGlbW1vLEPCioCD/boQQz68SJUpk2V9neFq/izKmy2T0B2lpaVlOmcnoNzLWEDlw4AAJCQlZtuni4kL79u1xcnKy/F5/cFqkvb09ZrOZW7duUatWrUzv655FGX8/SmWx/25UCCGyVOyTISqVig4dOrB69WoCAwMpU6ZMYYeUpf379+Pk5MT8+fMtnVJYWNhjz2vQoAEnTpzg2rVrlpEnNjY21KxZEwA3N7dskyEZ6wno9Xqr8nv37lktyOrm5pbtYq6urq4YDAbi4+Nxc3OzlGdsXefq6gpAhQoVWLhwIXq9ntOnT/PFF1/w9ttvc+TIEZydnVEoFAwZMoQ2bdpkusbDiRbx/FCpVJY3o3nx8HaKGW/e3dzcrLbWvXHjhiQQhBCiiMtYn8PR0TFX5/Xo0SNTX+Dl5UWXLl1ytLVuSkoK0dHRj1zHTQghxLPlufht3r17d8LDw3n55ZcLO5RHSktLy/TN8sM7sWSlR48erFq1imnTprFs2bIsd5TJjp+fH3B/p5g6deoA9xdyvXTpEjVq1LDUa9iwoWW9lKymytStWxe4v3Xxg9uT/vDDD5QqVSrTuiZqtZoGDRowePBghg4dSlRUFP7+/gQEBPDvv/9akjlC5Ad7e3v69++fbZ0Hd4oSQghR/OSkLxBCCPH8eC6SIVWrVs3V9JjC0LhxY9auXcunn35K27ZtOX36NDt27Hjsed7e3syaNYuRI0fSs2dPevXqhb+/PzqdjsuXL3P8+HFLwiMrfn5+1K5dmy+//BJnZ2dUKhUrVqzIlPB49913OXToEK+//joDBw7E29uba9eukZqayqBBg6hSpQrt2rVjxowZpKWlUalSJX788UeOHj1qWUz24sWLzJw5k44dO1KmTBmSkpJYvnw5pUqVomzZsgB8+OGH9OvXj/fff5+XXnoJFxcXIiIiOHbsGN26dSMoKOgJXmUhhBBCCCGEEOI5SYY8C5o3b87o0aNZv349W7dupU6dOixfvpx27do99tw2bdqwdetWvvrqK7788kvu3r2Lra0tlStXJiQkhF69emV7/pw5c5g4cSLjx4/H29ub999/nx07dlh9U16+fHn+7//+j7lz5zJlyhSMRiPly5dn8ODBljqzZ89m3rx5rFq1ivj4eMqXL8/s2bMtI3K8vb3x8vJi+fLlREZG4uzsTL169Zg9e7Zlt5s6derw7bffsmjRIsaPH49er8fPz4/g4GDKlSuXl5dWCCGEEEIIIYSwojDndTN2IZ4RGbvQFPbUm+K2qKXcT9FVnO4F5H6KuuJwP0Wln4Di8XpmKE73AnI/RV1xup/idC9Q/O5HFB+yfLQQQgghhBBCCCGeK5IMEUIIIYQQQgghxHNFkiFCCCGEEEIIIYR4rkgyRAghhBBCCCGEEM+VIpUM6dq1K1qtlpMnTxZ2KPnu5MmTaLVayyJtBeXw4cO88cYbBAUFERAQQNu2bRk9ejTXr1/PdVtarZZVq1ZZlc2aNYsmTZpQpUoVPv/88xy3tWjRIv78889cxyCEEEIIIYQQQjypIrO17rVr1/jnn38A2LVrF0FBQYUcUf6qXr06GzdupGLFigV2zT179jBq1Ci6du3KwIEDUavVXLt2jR9//JFr167h7+//RO0fPXqUVatWMX78eGrXro2Pj0+Oz128eDEODg7UqVPniWIQeRMZGcmSJUu4evUqqampmM1mVCoV3t7evPTSS7Ro0QInJ6fCDlMIIUQhS01NZePGjZw9e5bo6GgAvL29qVWrFj179sTe3r6QIxRCCCHypsgkQ3bt2oVKpaJBgwbs27ePSZMmodFoCjusfOPk5ERAQECBXnPdunUEBQUxY8YMS1njxo3p27cvJpPpidu/du0aAH379kWpLFKDjMQjXLt2ja1bt/Lrr78CMHPmTADGjh2LwWAgPDyclStX8vXXX9O8eXM6d+5M+fLlCzFiIYQQheXixYt8/vnnJCYmWvUXiYmJ/Pvvvxw8eJCJEyfywgsvFHKkQgghRO4VmU+wu3fvJjg4mDfffJOEhASOHDliOXb79m20Wi3bt29n4sSJ1KtXj+DgYMuUjT179tCuXTvq1KnDu+++S0JCguXcjOkpR44cYfjw4QQGBtK8eXN27NgBwDfffEOLFi2oX78+EyZMID093Squy5cv89ZbbxEYGEidOnUYOnQoN2/etKqj1Wr56quvWLhwIY0aNSIoKIjx48eTkpKSKY6MaTIZ97R3716rtqZOnUqrVq0sz7du3YpWq+Xs2bP069eP2rVr065dO44ePYrJZGL+/Pk0btyYhg0bMnfuXKskR2JiIt7e3lm+3g8mL1q1asXUqVOtju/duxetVsvt27ezPD8kJITp06cDULVqVcv0pqioKMaPH0/r1q2pVasWL774Il988YXV66rVaoH7U2y0Wq3V1Ciz2cyqVato164dNWrUoHXr1nz99ddZxiByJjU1lV9++YXx48fzwQcfWBIh2bExGjhw4ADvv/8+EydO5PDhw6SlpRVAtEIIIQpTQkIC+/bt48MPP2TcuHEkJiZmW/fDDz9k/Pjx7N+/n6SkpAKMVAghhHgyRWJkyJkzZwgNDWXo0KE0btwYd3d3du7cSZs2bazqzZ8/n/bt27NgwQL279/PrFmziIuL47fffmPMmDEkJSXx2WefMXv2bD799FOrc6dMmcKrr75Kr169+P777xk3bhyXL1/mypUrTJkyhdDQUGbMmEGZMmV4++23AQgPD6dPnz6UKlWKGTNmYDQaWbRoEX369GHnzp14eHhY2t+wYQN169ZlxowZXL9+ndmzZ+Pp6cno0aPz5TUaN24cvXv3ZuDAgaxYsYIRI0bQrVs3kpKSmDFjBn/99ReLFi3ihRdeoHPnzsD9qTk//vgja9asoW3btpQuXTpfYgGYPHky//d//8e6devYuHEjAJUqVSIsLAw3NzfGjx+Pi4sLN27cYNGiRURHR1uSJxs3bqRnz56EhITQqVMny7kAn3/+OZs2beLtt9+mdu3a/Pnnn8yZMwdbW1t69+6db/EXlvT0dO7evUt8fDzp6emkpaWh0+lITU1Fp9ORnp6OwWDAbDYDoFKp0Gg02NraYmtri52dHXZ2dmg0GtRqNSqVCqVSiclkwmg0kpaWRmJiIjExMdy+fZvLly9z4cIFDAYDABXsbIg1mIg3ZD8yyE6poJO7Pb8npXP+/HnOnz+P2saGqtWqodVqKV26NB4eHpY4hRBCPD337t0jPT0dvV5Peno6Op3O0mekp6djNBoxm80olUpUKhVqtdrSb2T0ITY2NqhUKhQKBWazGb1eT2pqKomJiURFRREaGsrFixe5fPkyJpMJBfffJBoeE5tGARcuXODChQssXbqEKlWqUqVKFcqUKYOLiwtGo7EAXiEhhBAi94pEMmTXrl1oNBpefPFFbGxs6NChA1u2bCEpKclq3YLAwEDGjRsHQHBwMD/99BMbNmzg4MGDuLu7A3Dp0iU2b96cKRnSoUMHhg0bBkCtWrX4+eef2bNnDz/99JNlOs6pU6fYu3evJRny9ddfo9frWb16tSXxkTEyY8OGDQwfPtzSvpeXF3PnzgWgWbNmnDt3jn379uVbMiQkJMSSDPD19aVz586cO3eO77//HoCmTZty8OBB9u7da0mGfPDBB1y9epUZM2YwY8YMvL29adGiBW+88QZVqlR5ongqVapEiRIlAKym/2i1WsaOHWt5XqdOHezt7Rk3bhyTJk3C3t7eUr9EiRJW5966dYv169czZcoUevbsCUCjRo1ISUnhyy+/pGfPns/0dJykpCRmzZpV4AkEWwU0dLWlmoMGVxslX0c8+lu+DCkmMxXs1VSwVxNnMPJPsp5TiTrOnj3L2bNnLfV69+5NtWrVnmb4QgjxXNu8eTPr168vsOu5qBTUdbOjioOaVeGJ8Jguy81GSTcvRy6k6Pk9Ucfff//N33//bTlet25datSo8ZSjFkIIIXKv0D9ZGo1GfvzxR1q0aIGzszMAnTt3RqfT8dNPP1nVbdSokeVnlUpFmTJlqFKliiURAlC+fHkSEhJITk5+5LnOzs54eHhQr149q3VJypcvT3h4uOX577//TnBwsNUIkFKlShEYGMjvv/9u1X7jxo2tnleqVImIiIgcvw6P82D8GWs4NGzY0KqOv7+/Vfy+vr5s3ryZb775hkGDBlG6dGm2bNlC9+7dOXz4cL7F9iCz2czXX39Nx44dqVWrFtWrV2f06NEYDAZCQ0OzPffYsWMAvPjiixgMBsujYcOGREdHW93bs8je3t4yRagg6czwT4qeCynpJBtNVLRXP/acyv+tk2Q0cSFZzz8pevQPvSF2cHDAzc3tKUQshBAiQ0Gvx5FgNPNPsp5LKXr87R7/nVlZWxsuZvQxpsyZk1KlSj2NMIUQQognVugjQ3799Vfu3r1Ly5YtLWt9VKpUCT8/P3bt2kW3bt0sdTOSJRnUajUODg6ZygB0Oh2Ojo6PPFej0eDi4pLp3AfXtkhISKBq1aqZYvby8sq0Ne3j2npSD8afkcDJyTWVSiVBQUGW3Xn++ecf3njjDebPn0/z5s3zLb4Ma9euZebMmQwcOJCgoCBcXFw4d+4cU6dORafTZXtuXFwcZrOZ4ODgLI+Hh4c/02+qVCoVr732GlWrVs307xbuJ5IeNU1GpVLl6lp6vZ47d+5w+fJl/vzzT/744w+O3NPxa4KOF+xtcFMpiTdmPVXG00ZJdUc1u++mcDFFjwmwtbWlcVA96tSpwwsvvECJEiVIT0/nwoULuX4dhBBC5FytWrXYvn37U71Geno6t2/f5uLFi/zxxx+cOXOGg/FpKLg/DSb9EaNDNAr4IykdM6C2saFBg3rUrVsXrVZLqVKl0Ov10k8IIYQosgo9GbJr1y4Axo8fz/jx462ORUVFWbZxKwyurq7ExMRkKo+JicHV1fWJ2ra1tQXuf2h90L17956o3cepVq0ajRs3thoZotFo8i2OvXv30qpVKz744ANLWcauM4/j6uqKQqHg22+/tSS1HvSkWwEXdQqFwjLH+0mp1WrKlStHuXLlaNu2LSkpKRw+fJg9e/Zw4b+L4joqFXz20Tj0JrBXKrBTKnBUKtCZTHwffX/x33LlytGpUyeaNGmSafvE/Ez2CSGEKDwajYYKFSpQoUIFOnbsSEJCAkePHmX//v2WL38enAKbId0MFStWpG3btjRp0iTTluwPv7cQQgghipJCTYakpqayf/9+2rRpQ9++fa2OxcbG8v7777Nnz55MC6kWlLp167Jx40bi4uIsU3HCw8M5ffo0Q4YMeaK2PT09UavVVomC9PR0fv/991yPAniUmJgYvLy8rMpMJhM3b960Kvfz88uUsMjJjiNZSUtLy5TIyEh4PUitVmcaKZIx7Sc+Pt5qRx3x5BwcHOjQoQPt2rXjzJkz/PDDD/z555+YMkaHmCHVZCaO+6NRgoPr06FDB2rVqoVCoSjU2IUQQhQsFxcXXnrpJV566SVu3rzJb7/9xuXLly1fUHl7e6PVamnQoAFlypQp5GiFEEKIvCnUZMjBgwdJSUkhJCTEMo3jQatWrWLXrl2Flgzp378/W7du5a233uLtt9+27Cbj6upKnz59nqhtpVJJ27Zt2bBhA+XKlcPd3Z1169bl6wfPgQMHUr58eVq2bEmpUqWIi4tjy5YtXLp0iY8++shSr127dnzyyScsXryYwMBADh06ZNkCOLcaNWrEN998w/r16ylfvjy7du3KtBUxQIUKFThw4AD16tXD3t4ef39//P396dOnDx9++CFvvfUWtWvXRq/Xc+PGDU6ePMmSJUvy/FqI+5RKJXXq1KFOnTokJCRw9uxZbt68SUpKCo6OjpQvX55atWpl+nZPCCHE8yljlKEQQghR3BRqMmTXrl2ULFkyy0QIQNeuXZk6dWqhDbMsUaIE69evZ9asWXz44YcoFAqCgoIYN26c1aKqefXxxx/z8ccf89lnn+Ho6MjAgQMpV64chw4devLggUGDBvHjjz+yYMECoqOjcXZ2pkKFCixatIgXX3zRUu+1117j1q1bfPfdd5bFT997770sh8Q+zjvvvENcXBwLFy4E7idaJk6caNmhJ8OkSZOYNm0agwYNIi0tjW+++YagoCAmTpyIv78/Gzdu5Msvv8TBwQF/f386dOjwZC+GyMTFxYUmTZrQpEmTwg5FCCGEEEIIIQqUwlzQ+3wKUcAyRrnUrFmzUONISUnhwoULj1xA9Vkj91N0Fad7Abmfoq443E9R6SegeLyeGYrTvYDcT1FXnO6nON0LFL/7EcVHoW+tK4QQQgghhBBCCFGQJBkihBBCCCGEEEKI54okQ4QQQgghhBBCCPFckWSIEEIIIYQQQgghniuSDMmFRYsWodVqadq0KSaTKdPxQYMGodVqGTJkCAAnT55Eq9XmepvavJ73JMaNG0enTp0yxZDVo3///pZ6W7duZdeuXVm2aTQa+e6773j11VcJCAigTp06vP766/zwww+Z6i5atIjAwMAs28numBBCCCGEEEIIkVuFurXus0itVhMXF8fJkydp2LChpTw2NpZjx45ZrZBcvXp1Nm7cSMWKFXN1jbye9zRMnz6dChUqWJU5Oztbft62bRsODg507tzZqo7JZGLkyJHs37+f119/nQ8++AC9Xs8PP/zAyJEjOX/+PB9++GGB3IMQouClpqayceNGzp8/T0xMzBO1ZTabMRgM2NjYoFAo8inCx/Py8qJGjRr07NkTe3v7AruuEEII8aSetB+WPlA8DyQZkktqtZqGDRuye/duq2TIjz/+iI+PD6VKlbKUOTk5ERAQkOtr5PW8p6Fy5cp52mrw22+/Zd++fXzyySf07t3bUt68eXO8vLxYuXIlQUFBNG/ePD/DFUIUAQkJCUyYMIHQ0NBcnztz5kwAxo4dm99h5Vp8fDxXr17ljz/+YNq0aVaJYCGEEKKoykk//Lj+VvpA8TyQaTJ50KlTJ3766SfS09MtZbt376Zjx45W31pmNd1Fq9Xy1VdfsXDhQho1akRQUBDjx48nJSXlseetWLGCOXPmEBwcTL169Zg5cyZms5njx4/zyiuvEBgYSN++fQkPD7eKd86cOXTu3JnAwECaNm3KqFGjiIqKeuLXISQkhFOnTnHo0CHLFJpFixYBsHbtWsqVK0ePHj0ynff222/j7OzM119//cQxCCGKnt27d+cpEVJUhYaGsnv37sIOQwghhMiR/OyHpQ8UxZmMDMmDVq1aMXHiRI4cOUKbNm0ICwvj9OnTTJ48mbNnzz72/A0bNlC3bl1mzJjB9evXmT17Np6enowePfqx5wUHBzN79mz++usvFi1ahNFo5Pjx4wwdOhS1Ws1nn33GhAkTWL16teW8u3fvMmTIEHx8fIiNjWXNmjWEhISwZ88ebGyy/ydgMpkwGAxWZUqlEqVSyeTJkxkzZgx2dnaWrLKfnx/h4eHcunWL/v37o1KpMrXp7OxMUFAQR48etQx9z/DwtTJiEKK4MJvNln/TZrMZs9mc6edz586xZcsWUlNTs23nUeVms5m0tDRsbW1RKpXZ1n1cWW6fA088LaYo2rJlC0eOHAF47FSdRx3PKDeZTKSnp2Nra4tCobCU29nZ0bFjR6pUqYJSqbQcUygUVs+VyvvfY2SUPVw3q/OEEEI8HRn97sP9+4N/ZlU/q3MzHsnJycTHxxMeHo5Go+Gff/5h3759pKWlZTonq58TExPz9R6///57du7cmakvetTPtra2tGrViipVqqBWqzEajcTFxREbG4ter0etVqNSqSwP6adEYZFkSB7Y2dnRpk0bdu/ebfmzYsWKVKlSJUfne3l5MXfuXACaNWvGuXPn2Ldv32OTIb6+vpYhbU2bNuXgwYN888037Nmzx7K+SGRkJJ9++ikJCQm4uLgA99f9yGA0GgkMDKRZs2acOHGCJk2aZHvNrEZ2vPvuuwwfPpxKlSrh5OSEg4OD1bSeM2fOAFCyZMlHtluyZEl0Oh3x8fF4eXkBkJKSQvXq1bOs/+BaLELkxPbt22X0kcg3BoMh06i7p2HBggVP/RpPg52dHTNnzqRcuXKFHYoQQuRYREQEo0aNshqhLTIzm83ZfkGTlfx+DzZw4ECrzR6EyA+SDMmjzp078+6775KcnMzu3bszLSCancaNG1s9r1SpEvv27XvseQ+uUQLg7+9PTEyM1UKr5cuXB+7/cs9Ihhw+fJilS5dy5coVkpKSLHVv3Ljx2GTIzJkzMy3k6uPj89hYc+rBTLCdnR3r16/PVOf777+X4Xki1zK+PRdCPH3y/00I8Sx6cLSdKNpk9Ih4GiQZkkeNGjXC0dGRJUuWcPnyZZYsWZLjczOSFBnUarXV+iO5OS+rMgCdTgfA2bNnGTZsGK1bt2bQoEF4enqiUCjo0aOHpU52KlasmOsFVH19fQG4c+fOI+vcuXMHW1tbXF1dLWVKpTLLax06dChX1xcC4OWXX+bll1/Ocf2UlBQuXLhA1apVC2wk0oNDXR9+ntX0mYePP6pOSkoKly9fplKlStjZ2WV5TsZQ3oxhtQ8+f7D8wWNGo/GR5z7YfnJyMuvXry8202W8vb0ZOHAgDg4OVtNaHpyO8uDzh3/OeKOdUabT6bh+/ToVK1a0/Ft7eOpLdtNfHmzvUVNlCnJ6TMb/HW9v7wK5nhBC5BdfX98sv4jLi+z65ofrZVXfaDRiNpsxGo0kJydz6dIlKlSogFqtxmAwYDAY0Ov16PV60tPTLQ+dTpfpkZCQwKlTp/JlxItGo6Fy5co4ODigVquxtbXF1tYWjUZj+fnhsgf/zJgmc/PmTapWrYqzszM2NjaWhyQ5RGGSZEgeqVQqOnTowOrVqwkMDKRMmTKFHVKW9u/fj5OTE/Pnz7e8gQ4LC3uq1yxRogRlypTh0KFDjB07NlPGPSkpiVOnTlG3bt3HrlkiRHH24Afm/GRra4uLiwuenp6FNsUsKCgoT1v6ZUzrc3Nzs5QVt611U1JSSEpKolSpUjIFUAghion87NM1Gg2urq74+PjkuZ943Na6WfW3D8rPPjAlJYV79+7h5uYm/Z4oUuST6BPo3r074eHhufr2uaClpaWhVqutfjnv2rUr39pXq9VZjjDp378/n376KZs2baJnz55Wx1asWEFCQgL9+/fPtziEEEWLvb19vv0fL4xRO0IIIcSzLD/7YSGKK0mGPIGqVavmanpMYWjcuDFr167l008/pW3btpw+fZodO3bk+PwrV65YhsZn0Gg0VKtWDYAKFSqwfft2Dh48iLe3Nz4+Pvj6+vL6669z6tQppk6dytWrV2nZsiUGg4EffviBbdu28dZbb9G8efN8vVchhBBCCCGEECInJBlSzDVv3pzRo0ezfv16tm7dSp06dVi+fDnt2rXL0fnjx4/PVFaqVCkOHjwIwKBBg7h16xZjx44lISHBstOMUqlk3rx5fP/992zatIlNmzahUCioWrUq8+bNo2PHjvl6n0IIIYQQQgghRE4pzFmt7CNEMXLu3DmAXC8Em9+K21B/uZ+iqzjdC8j9FHXF4X6KSj8BxeP1zFCc7gXkfoq64nQ/xeleoPjdjyg+ZC8pIYQQQgghhBBCPFckGSKEEEIIIYQQQojniiRDhBBCCCGEEEII8VyRZIgQQgghhBBCCCGeK7KbjBDFRFJSEmFhYdja2lKmTBlUKlVhhySEEEIIIYQQRZIkQ54TixYtYvHixZbn7u7uaLVahg8fTr169Z6o7du3b9O6dWsWLFhA+/btAQgJCcHBwYHly5c/Udvi8f7991++++47fv/9dzI2h3J2cuLFdu3o2rUrTk5OhRyhEEKIosJsNhMTE4NOp8PT0xN7e/vCDkkIIYQoFJIMeY7Y2dmxdu1aACIiIli6dCn9+/dny5YtaLXafL3W5MmTUSplFtbTlJSUxNdff83+/fuB+3PebBRgBpKTktiyZQv79u6lX//+tG7dWv4+hBDiOZWamsratWs5ceIE9+7dsyTO4f6XI506daJNmza4uroWYpRCCCFEwZJkyHNEqVQSEBBgeV6rVi1atWrFxo0bmTRpklVds9mMXq9Ho9Hk6VqVKlV6klBFNhITE/n555/ZsmULycnJAMycOROAsWPHWtVNTk7myy+/ZN/evfR54w0CAgJQKBQFHrMQQojCcefOHcaOHUtiYqKl7ME+Iy4ujnXr1rFhwwYaNGhAy5YtqVOnDmq1urBCFkIIIQqEJEOeYyVLlsTd3Z3bt28zbtw4zp8/z5gxY5g7dy7//vsvs2fPpkOHDuzfv5/Fixdz7do1XFxcePHFFxk9ejSOjo6PbPvhaTKLFi1i9erVfPfdd0yZMoV//vmHMmXKMHbsWJo2bWp17tatW1mzZg03btzAzc2Nbt26MXz4cGxsit8/V7PZTGJiIvfu3SMxMRGdTofRaARApVJhMpnQ6XQkJCQQERHB1atXuXDhAkajkZysCGIGvG2UXL12jSlTplCmTBmaNWtG7dq1KVeuHLa2tk/1/oQQQuQ/vV5PcnIyOp0Ok8mEWq3G3t4eOzs74P7IwRs3bnDixAl++uknS7+SHTtMnDhxghMnTmBvZ0edunUJCAigatWqlChRQtahEkIIUewUv0+XIseSkpK4d+8ePj4+GAwGoqKi+Pzzzxk6dCh+fn6UKFGCAwcO8O6779K+fXtGjhzJ7du3mTt3LtevX+frr7/O1fX0ej1jxoyhb9++DBs2jBUrVjBixAgOHjyIu7s7AGvWrGH27Nn069ePcePGce3aNebNm4fRaGT06NFP4VUoWAaDgRkzZnD27Nk8t1FCo6Kqsx3nktOJ1pseWz/OYMJXrcROqeD27VA2bNjAhg0brOqMHj2aJk2a5DkmIYQQT4der2fGjBn88ccfeTo/pxMk003gaaMk3WzGlK7j119/5ddff7Wq069fP7p27ZqnOIQQQoiiRpIhzxmDwQDcXzNk5syZGI1G2rVrx549e7h37x4rV66kVq1alvojR46kZs2azJ8/31Lm6urKBx98wMmTJwkKCsrxtfV6PaNHj6Z58+YAlC1blhdffJEjR47wyiuvkJSUxMKFCxk4cCCjRo0CoHHjxqhUKmbNmsVbb71lSZo8q3Q63RMlQkprVFSyV/OCvZrzyek5OscAROpN+KmVlNCouK3L/A3hxYsXJRkihBBFUHJycp4TIQC2SgWpJvNj6xmAu4b7CXY3lRIbhRnDQ6f9/fffkgwRQghRbEgy5DmSkpJC9erVLc9dXV2ZNGkSTZs2Zc+ePbi7u1slQpKTk7lw4QIffvihVTvt27dn7Nix/P7777lKhiiVSho2bGh5Xq5cOdRqNZGRkQCcPn2alJQU2rdvb0naAAQHB5OWlsaVK1do0KBBru+7KHF0dGTlypXodDoSExNJSEggLi6O+Ph4q2kyCoUChUKB2WwmPT39/jSZ8HBuR0VxO93I4XtpeNjk7Ps+WwUYzRDx31Ek/v7+1K5dG39/f3x9fXF1dcXPz+9p3rYQQog8cnNz47vvvuPevXukpKSQkJBAQkICSUlJpKWlAWBjY2OZJqNQKEhMTOTGjRucOnWKuLi4HF1HDZgAIxBvNGGr0VCrRg20Wi3lypXDx8eHMmXKPLX7FEIIIQqaJEOeI3Z2dqxfvx6FQoG7uzslSpSw2mHE09PTqn5iYiJmsxkvLy+rchsbG9zc3Lh3716ur//wgqxqtRqdTgdgecP2qG+dwsPDc3W9osrBwSHTa5pT9+7d4/fff+fHH37g6rVrj62vVoDODCqlkratW/Pyyy/Lm1khhHjG2Nvb52kL3MGDB7N//36++uorqy8ZsqIHbDUamjRsSJMmTahdu3aeF1EXQgghngWSDHmOKJVKatas+cjjD+8y4uzstB44FgAAEpBJREFUjEKh4O7du1blBoOB+Pj4fN+CL6O9xYsXZzlSoXTp0vl6vWeRq6srrVu3plWrVpw4cYKVK1dy9+5dxo8di40C7JT399ZVKCDVZEZvhjr/397dR2Vd338cfyFBdBRqpnmmm6tYFwoyCYcIKWiGnWWRlGanWDve5tTR1ON2mqnzaFs3p7kzNY8DWkdLt270LG/YySJRG+Q5w8LNsR3AE8iWIZtyK4q8f3/046pLsJVxcX2/XM/HORzlc32uL+8Xx+v7vnxfd4mJmjNnjoYNGxbo8gEAvSg0NFR33nmnUlNT9dxzz+mvf/2r2tvbu3zy2C233KIpU6Zo/PjxVzR0AQDAjRiG4LL69++vkSNHqqCgQLNmzfKuv/nmm2pvb9d3v/vdHv15iYmJuuaaa/TRRx8pIyOjR4/d14SEhCglJUW33nqrXn/9db3xxz+q7fx5yf7/Bd4mDRs6VN9/5BElJyfzcboAEMQiIyP185//XBcvXlR5ebkqKirU1tamwYMHa9SoURo8eHCgSwQAoNcxDMHnWrx4sRYtWqSlS5dq2rRpqqmp0a9+9SulpKR8qfcL+SIiIyOVk5OjZ599Vh999JGSk5PVr18/1dTU6O2339aGDRt4xOoSERERevjhh5WZmakjR46ourpaV199teLi4hQfH+/zMigAQHALDQ1VXFycz/uHAQAQrBiG4HNNnjxZGzZs0KZNm7Rw4UJFRUUpMzPTbx9zO3v2bA0ZMkS/+93v9NJLL+mqq67S8OHDNXHiRIWFhfnlZ/YFkZGRmjx5cqDLAAAAAABXCDGz//15a4CLlZaWyswC/kZwZqYLFy4oLCysT7xshTzO1ZeySORxOifkCQ8PV0xMzBVf3yl9QnLG77On9KUsEnmcri/l6UtZJGfk+ap9An0TzwxBn+eUJhISEuKIO9o9hTzO1ZeySORxur6Qxyl9Quobv89OfSmLRB6n60t5+lIWqe/lQd/BM0MAAAAAAEBQ4d0VAQAAAABAUGEYAgAAAAAAggrDEAAAAAAAEFQYhgAAAAAAgKDCMAQAAAAAAAQVhiEAAAAAACCoMAwBAAAAAABBhWEIAAAAAAAIKgxDAAAAAABAUGEYAgAAAAAAggrDEAAAAAAAEFQYhgAAAAAAgKDCMATwsxMnTmjOnDlKSEhQSkqK1q1bp3PnzgW6LB8FBQVauHCh0tPTlZCQoHvuuUfbt29XR0eHz76ioiJNmzZN8fHxysjI0Msvv9zt8fLz83X77bcrPj5e999/v957773eiHFZzc3NSktLU0xMjI4dO+ZzmZsyvfrqq8rMzFR8fLxSUlK0YMECn8vdkuWtt97SjBkzlJiYqNTUVC1evFhVVVVd9jkxz4cffqhVq1bp3nvvVWxsrO6+++5u9/Vk7U1NTVq1apWSk5N16623asGCBaqtre2VPBcvXlRubq6ys7M1btw4JSUl6eGHH1ZxcbEj87gZvSKwvYI+4csJWegVX652f51b6RPo0wyA35w9e9YmTJhgM2fOtKKiItu1a5eNHTvWli1bFujSfMyYMcMee+wx27NnjxUXF9uvf/1ri42Ntaeeesq7p7S01GJjY+3xxx+34uJi27Rpk40YMcJeeeUVn2Pl5eVZXFyc5eXl2Z///GdbsmSJxcfHW3l5eW/H8nrmmWcsNTXVPB6PlZWVedfdlOk3v/mNJSYm2m9/+1t777337M0337SVK1e6Lsu7775rMTExtnz5cjt8+LDt27fP7rrrLktLS7PGxkbH59m/f7+lpaXZj370I7v77rtt6tSpXfb0dO3z58+32267zXbv3m3vvPOOZWVlWUZGhrW2tvo9T1NTk40ZM8bWrl1rhYWFdvDgQVuyZInFxMRYYWGh4/K4Fb0i8L2CPuGsLPSKL1+7v86t9An0ZQxDAD/asmWLjR492urr671rb7zxhnk8HquoqAhgZb4+W1+nX/ziFxYfH29tbW1mZjZnzhybPn26z54nnnjCbrvtNrt48aKZmbW1tdmYMWPs6aef9u5pb2+3733ve/bjH//Yjwkur6KiwhISEmzHjh1d7uS6JVNFRYWNHDnSDh06dNk9bsnys5/9zCZNmmQdHR3etQ8++MA8Ho8dOHDAu+bUPJ0/28zspz/9abd3cHuy9vfff7/L76a2ttZiY2Nt+/btfs/T3t5uZ86c8Vnr6OiwrKwsy87O9q45JY9b0SsC2yvoE87KYkavcFKvoE+gL+NlMoAfHTx4UCkpKRo4cKB37c4771R4eLiKiooCWJmvz9bXaeTIkWpra9OZM2d0/vx5lZSUaOrUqT577rnnHtXV1en48eOSpNLSUjU2Nvo8hTI0NFR33XWXioqKZGb+DdKNJ598Ug8++KBuuukmn3U3Zdq5c6e++c1vavz48d1e7qYs7e3t6t+/v0JCQrxrkZGRPnucnKdfv89vmz1de1FRkaKiopSWlubdN3ToUCUmJvbIOeR/5QkNDdW1117rsxYSEqIRI0bo448/9q45JY9b0SsC2yvoE87KItErnNQr6BPoyxiGAH5UWVmp6Ohon7Xw8HANHz5clZWVAarqi/nLX/6i6667Ttdff72qq6t14cIF3XzzzT57vv3tb0uSN0vnn5fui46OVnNzs06dOtULlX/qT3/6k8rLy7Vo0aIul7kp0wcffCCPx6NNmzYpJSVFo0aNUnZ2tv7+979LcleW6dOnq6qqStu2bVNDQ4NOnjypp59+WtHR0UpJSZHkrjyX6unaKysrddNNN/n8h6DzeIE6h3R0dOjo0aM+5zY353ECesUnAnHbpU84L4tEr3B7r6BPwC0YhgB+1NDQoKioqC7rUVFROnv2bAAq+mKOHTumnTt36gc/+IFCQ0O9tV6apfP7zssbGhoUHh6uiIgIn32djxicOXPGz5V/qrW1VU899ZSWLl2qAQMGdLncTZnq6up0+PBh7d69W2vWrNGGDRvU2tqqWbNmqaGhwVVZkpKStHHjRq1fv15JSUmaPHmyampq9MILLyg8PNynXjfkuVRP197Q0NDl0dDO4wXqHLJt2zadOHFCs2bN8q65OY8T0Cs+0du3XfqEM7NI9Aq39wr6BNyCYQgQAGbWZeLtFHV1dcrJyVF8fLzmzZvnc9nlav7send7Op/62JuZN2/erOuvv1733Xff5+5zQyYzU0tLizZs2KApU6Zo0qRJ2rx5s5qbm/WHP/zhc+u8dD3QWUpLS7V8+XLdf//9evHFF7Vx40ZFRERo3rx5ampq8tnrhjyX05O1f5Fj9ZYjR47o2Wef1ezZs5WUlPQ/63F6HqejV/gXfULdrgc6i0SvcHOvoE/ATRiGAH4UFRWlhoaGLuuNjY3dPgoYaI2NjZo3b54iIiK0efNmhYWFSfp0an/pRL4zW2eWqKgotbW1qa2trdt9l76m1F9qa2v1wgsvKCcnR01NTWpoaFBLS4skqaWlRc3Nza7KdO2112rQoEG65ZZbvGs33HCDbr75ZlVUVLgqy7p16zRu3DitWLFCKSkpysjIUG5urk6cOKFXX33Vpw435LlUT9d+uXPI5Z5J4E/l5eVauHCh7rjjDi1fvtznMjfmcRJ6he++3rjt0id8a3RSFole4dZeQZ+A2zAMAfwoOjq6y+sbz58/r+rq6i6vDw+0trY2/fCHP9Tp06eVl5enr33ta97Lhg8frrCwMFVVVflcp6KiQpK8WTr/vDRzZWWl+vfvryFDhvgzgtfJkyd14cIFzZ8/X0lJSUpKStKCBQskSY888ohmzZrlqkyX+7diZurXr5+rslRWVmrEiBE+awMHDtQNN9yg6upqSe779/ZZPV17dHS0Tpw40eUN/ioqKnr1HFJdXa25c+cqNjZWzzzzTJdH59yWx2noFfJ+31u3XfrEJ5yYpfPn0SvcdW6lT8CNGIYAfpSWlqaSkhL997//9a7t379f58+fV3p6egAr89Xe3q7HHntM5eXlysvL07Bhw3wuDw8P17hx41RQUOCzvmfPHg0ePFixsbGSpMTEREVGRmrfvn3ePRcvXlRBQYHS09N77emNI0eO1NatW32+Hn/8cUnSmjVrtHr1aldlmjhxok6fPq1//vOf3rVTp06pqqpKMTExrsoydOhQ/e1vf/NZq6ur08cff+z9d+emPJfq6drT09PV0NCgQ4cOeff9+9//Vmlpaa+dQ+rq6jR79mwNGjRIzz//vPf1+p/lpjxORK/o/dsufcK5WSR6hdt6BX0CruXvz+4FgtnZs2dtwoQJ9uCDD9rBgwdt165dlpycbMuWLQt0aT5WrlxpHo/HcnNz7ejRoz5fjY2NZmZWWlpqsbGxtmLFCispKbHnn3/eRowYYa+88orPsfLy8iwuLs7y8/OtuLjYli5davHx8VZeXh6IaF4lJSXm8XisrKzMu+aWTO3t7ZaVlWVTpkyxvXv32v79+23atGk2YcIEa25udlWWbdu2mcfjsTVr1tjhw4dt3759du+991pSUpKdOnXKu8+peVpaWqygoMAKCgosOzvb0tPTvd/X19f7pfb58+fb+PHjbc+ePXbgwAHLysqyjIwMa21t9Xue1tZWy8zMtISEBNu/f3+X84PT8rgVvcIZvYI+4YwsZvSKK6ndX+dW+gT6MoYhgJ9VVVXZ7NmzbfTo0ZacnGxr16513Ml80qRJ5vF4uv0qKSnx7jtw4IBlZmZaXFycTZ482V566aUux+ro6LDc3FybOHGijRo1yu677z4rLi7uzTjd6u5Orpl7Mp0+fdqWLl1qY8aMsdGjR9vcuXOtsrLSZ48bsnR0dNjvf/977x2n1NRUe/TRR7u9Q+rEPDU1Nb1+W2lsbLQnnnjCkpKSLCEhwR599FE7efJkr+T5vMs9Ho/j8rgZvSLwvYI+8QknZKFXfPna/XVupU+gLwsxu+QFWQAAAAAAAH0Y7xkCAAAAAACCCsMQAAAAAAAQVBiGAAAAAACAoMIwBAAAAAAABBWGIQAAAAAAIKgwDAEAAAAAAEGFYQgAAAAAAAgqDEMAAAAAAEBQYRgCAC61e/duvfjii1/pGC+//LJ27tzZMwUBAByHXgEA3WMYAgAutWfPHm3duvUrHWPHjh3atWtXD1UEAHAaegUAdI9hCAAAAAAACCohZmaBLgIA0NV//vMfrV+/XgcPHlR9fb0GDBigG2+8UTk5Odq8ebOOHDnS5Tr/+Mc/JEkbN25UUVGRPvzwQ7W3t+tb3/qWHnroIU2fPl0hISGSpNtvv121tbU+1x82bJgKCwv9Hw4A0CPoFQBwZa4KdAEAgO4tX75cx48f15IlS3TjjTeqoaFBx48f15kzZ7R69WqtXLlSNTU12rhxY5fr1tbWaubMmRo6dKgk6f3339e6det06tQpLV68WNInd4JzcnIUGRmp1atXS5LCw8N7LyAA4CujVwDAlWEYAgAOVVpaqhkzZuiBBx7wrt1xxx3ev0dFRSk8PFwJCQldrvvLX/7S+/eOjg6NHTtWZqatW7dq0aJFCgkJUWxsrCIiIjRgwIBujwEAcD56BQBcGYYhAOBQ3/nOd7Rr1y5dd911Sk1NVVxcnMLCwr7QdYuLi7VlyxYdO3ZMTU1NPpfV19dr0KBB/igZANDL6BUAcGV4A1UAcKj169dr2rRpeu211zRz5kyNHTtWP/nJT1RXV/e51ysrK9OcOXMkSWvXrtWOHTv02muvacGCBZKkc+fO+b12AEDvoFcAwJXhmSEA4FADBw7UihUrtGLFCv3rX/9SYWGhnnvuOdXX1ys/P/+y19u7d6+uuuoqbdmyRVdffbV3/a233uqNsgEAvYheAQBXhmeGAIALDB06VNnZ2UpNTdXx48clffIGdt09chcSEqLQ0FD16/fpKf7cuXN64403uuy93DEAAO5DrwCAL45hCAA4UGNjo7KyspSfn6933nlHR44cUX5+vg4dOqTU1FRJksfjUX19vbZv366ysjIdO3ZMkpSenq6WlhYtW7ZM7777rvbu3auHHnqo23f/93g8Ki8v1759+1RWVub9uEUAgPPRKwDgyoWYmQW6CACAr/Pnz+vJJ5/U0aNHVVtbq/b2dn3961/X1KlTNXfuXF1zzTU6e/asVq5cqeLiYjU2NsrMvHdQX3/9deXm5qq2tlZDhgzRAw884H0q9dtvv61vfOMbkj75WMVVq1bp6NGjam5u1rBhw1RYWBjI6ACAL4heAQBXjmEIAAAAAAAIKrxMBgAAAAAABBWGIQAAAAAAIKgwDAEAAAAAAEGFYQgAAAAAAAgqDEMAAAAAAEBQYRgCAAAAAACCCsMQAAAAAAAQVBiGAAAAAACAoMIwBAAAAAAABBWGIQAAAAAAIKgwDAEAAAAAAEHl/wACnHbWSci8HAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1120.25x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "media_color = '#d6604d'\n",
    "genetics_color = \"#4393c3\"\n",
    "\n",
    "sns.set_theme(style=\"whitegrid\")\n",
    "\n",
    "g1 = sns.catplot(data = dist_performance.sort_values(by = 'check'), y = 'group', x = 'stat', hue = 'check', \n",
    "                 kind = 'violin', \n",
    "                 aspect = 1, \n",
    "                 hue_order=['media', 'genetics'], palette = [media_color, genetics_color], \n",
    "                 col = 'method', \n",
    "                 row = 'test')\n",
    "\n",
    "# rotate xtick labels\n",
    "#g1.set_xticklabels(rotation=90)\n",
    "# add horizontal grid lines\n",
    "#g1.ax.yaxis.grid(True)\n",
    "# Add y axis line for 0.5\n",
    "#g1.ax.axhline(0.5, ls = '--',lw= 2, color = 'red', alpha = 0.5)\n",
    "g1.fig.savefig(results_folder + 'network_distance_performance.pdf', bbox_inches = 'tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'../results/new_pseudobulk_perc02/clustering_performance/bonobo_random/'"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_folder"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Clustering performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NME1_HRA1</th>\n",
       "      <th>PWR1_YFL012W</th>\n",
       "      <th>PWR1_YFR035C</th>\n",
       "      <th>Q0045_Q0050</th>\n",
       "      <th>Q0045_Q0075</th>\n",
       "      <th>Q0045_Q0130</th>\n",
       "      <th>Q0045_X21S_rRNA</th>\n",
       "      <th>Q0050_Q0075</th>\n",
       "      <th>Q0050_Q0085</th>\n",
       "      <th>Q0050_Q0110</th>\n",
       "      <th>...</th>\n",
       "      <th>snR86_YKL086W</th>\n",
       "      <th>snR87_YAL016C.B</th>\n",
       "      <th>snR87_YHL050C</th>\n",
       "      <th>snR87_YHR056W.A</th>\n",
       "      <th>snR87_YIR044C</th>\n",
       "      <th>snR9_YGR268C</th>\n",
       "      <th>snR9_YMR061W</th>\n",
       "      <th>snR9_YNL004W</th>\n",
       "      <th>snR9_YPL105C</th>\n",
       "      <th>snR9_YPL155C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>dal82_MinimalGlucose</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.921189</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.842767</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.889347</td>\n",
       "      <td>0.866927</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.832135</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dal81_YPDDiauxic</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.921188</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.894936</td>\n",
       "      <td>0.793110</td>\n",
       "      <td>0.911508</td>\n",
       "      <td>0.887268</td>\n",
       "      <td>0.809082</td>\n",
       "      <td>0.871608</td>\n",
       "      <td>0.830560</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gln3_YPD</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.887625</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.863078</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.891876</td>\n",
       "      <td>0.871815</td>\n",
       "      <td>0.802829</td>\n",
       "      <td>0.831230</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stp1_YPDRapa</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.921189</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.861894</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.891208</td>\n",
       "      <td>0.871173</td>\n",
       "      <td>0.801918</td>\n",
       "      <td>0.835407</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dal81_MinimalEtOH</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.921189</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.860010</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.882853</td>\n",
       "      <td>0.850195</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gcn4_YPDDiauxic</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.921188</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.872088</td>\n",
       "      <td>0.811269</td>\n",
       "      <td>0.900834</td>\n",
       "      <td>0.885799</td>\n",
       "      <td>0.833189</td>\n",
       "      <td>0.862844</td>\n",
       "      <td>0.833136</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dal81_Urea</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.921189</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.864021</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.889429</td>\n",
       "      <td>0.872044</td>\n",
       "      <td>0.804684</td>\n",
       "      <td>0.837632</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rtg1_Glutamine</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.921189</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.860100</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.888386</td>\n",
       "      <td>0.866798</td>\n",
       "      <td>0.803896</td>\n",
       "      <td>0.837666</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rtg1_MinimalGlucose</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.921189</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.843846</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.887918</td>\n",
       "      <td>0.855712</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.807024</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dal82_YPD</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.919331</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.863212</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.892030</td>\n",
       "      <td>0.871823</td>\n",
       "      <td>0.803765</td>\n",
       "      <td>0.837226</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>132 rows × 275047 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      NME1_HRA1  PWR1_YFL012W  PWR1_YFR035C  Q0045_Q0050  \\\n",
       "dal82_MinimalGlucose        0.0      0.921189           0.0     0.842767   \n",
       "dal81_YPDDiauxic            0.0      0.921188           0.0     0.894936   \n",
       "gln3_YPD                    0.0      0.887625           0.0     0.863078   \n",
       "stp1_YPDRapa                0.0      0.921189           0.0     0.861894   \n",
       "dal81_MinimalEtOH           0.0      0.921189           0.0     0.860010   \n",
       "...                         ...           ...           ...          ...   \n",
       "gcn4_YPDDiauxic             0.0      0.921188           0.0     0.872088   \n",
       "dal81_Urea                  0.0      0.921189           0.0     0.864021   \n",
       "rtg1_Glutamine              0.0      0.921189           0.0     0.860100   \n",
       "rtg1_MinimalGlucose         0.0      0.921189           0.0     0.843846   \n",
       "dal82_YPD                   0.0      0.919331           0.0     0.863212   \n",
       "\n",
       "                      Q0045_Q0075  Q0045_Q0130  Q0045_X21S_rRNA  Q0050_Q0075  \\\n",
       "dal82_MinimalGlucose     0.000000     0.889347         0.866927     0.000000   \n",
       "dal81_YPDDiauxic         0.793110     0.911508         0.887268     0.809082   \n",
       "gln3_YPD                 0.000000     0.891876         0.871815     0.802829   \n",
       "stp1_YPDRapa             0.000000     0.891208         0.871173     0.801918   \n",
       "dal81_MinimalEtOH        0.000000     0.882853         0.850195     0.000000   \n",
       "...                           ...          ...              ...          ...   \n",
       "gcn4_YPDDiauxic          0.811269     0.900834         0.885799     0.833189   \n",
       "dal81_Urea               0.000000     0.889429         0.872044     0.804684   \n",
       "rtg1_Glutamine           0.000000     0.888386         0.866798     0.803896   \n",
       "rtg1_MinimalGlucose      0.000000     0.887918         0.855712     0.000000   \n",
       "dal82_YPD                0.000000     0.892030         0.871823     0.803765   \n",
       "\n",
       "                      Q0050_Q0085  Q0050_Q0110  ...  snR86_YKL086W  \\\n",
       "dal82_MinimalGlucose     0.832135     0.000000  ...            0.0   \n",
       "dal81_YPDDiauxic         0.871608     0.830560  ...            0.0   \n",
       "gln3_YPD                 0.831230     0.000000  ...            0.0   \n",
       "stp1_YPDRapa             0.835407     0.000000  ...            0.0   \n",
       "dal81_MinimalEtOH        0.000000     0.000000  ...            0.0   \n",
       "...                           ...          ...  ...            ...   \n",
       "gcn4_YPDDiauxic          0.862844     0.833136  ...            0.0   \n",
       "dal81_Urea               0.837632     0.000000  ...            0.0   \n",
       "rtg1_Glutamine           0.837666     0.000000  ...            0.0   \n",
       "rtg1_MinimalGlucose      0.807024     0.000000  ...            0.0   \n",
       "dal82_YPD                0.837226     0.000000  ...            0.0   \n",
       "\n",
       "                      snR87_YAL016C.B  snR87_YHL050C  snR87_YHR056W.A  \\\n",
       "dal82_MinimalGlucose              0.0            0.0              0.0   \n",
       "dal81_YPDDiauxic                  0.0            0.0              0.0   \n",
       "gln3_YPD                          0.0            0.0              0.0   \n",
       "stp1_YPDRapa                      0.0            0.0              0.0   \n",
       "dal81_MinimalEtOH                 0.0            0.0              0.0   \n",
       "...                               ...            ...              ...   \n",
       "gcn4_YPDDiauxic                   0.0            0.0              0.0   \n",
       "dal81_Urea                        0.0            0.0              0.0   \n",
       "rtg1_Glutamine                    0.0            0.0              0.0   \n",
       "rtg1_MinimalGlucose               0.0            0.0              0.0   \n",
       "dal82_YPD                         0.0            0.0              0.0   \n",
       "\n",
       "                      snR87_YIR044C  snR9_YGR268C  snR9_YMR061W  snR9_YNL004W  \\\n",
       "dal82_MinimalGlucose            0.0           0.0           0.0           0.0   \n",
       "dal81_YPDDiauxic                0.0           0.0           0.0           0.0   \n",
       "gln3_YPD                        0.0           0.0           0.0           0.0   \n",
       "stp1_YPDRapa                    0.0           0.0           0.0           0.0   \n",
       "dal81_MinimalEtOH               0.0           0.0           0.0           0.0   \n",
       "...                             ...           ...           ...           ...   \n",
       "gcn4_YPDDiauxic                 0.0           0.0           0.0           0.0   \n",
       "dal81_Urea                      0.0           0.0           0.0           0.0   \n",
       "rtg1_Glutamine                  0.0           0.0           0.0           0.0   \n",
       "rtg1_MinimalGlucose             0.0           0.0           0.0           0.0   \n",
       "dal82_YPD                       0.0           0.0           0.0           0.0   \n",
       "\n",
       "                      snR9_YPL105C  snR9_YPL155C  \n",
       "dal82_MinimalGlucose           0.0           0.0  \n",
       "dal81_YPDDiauxic               0.0           0.0  \n",
       "gln3_YPD                       0.0           0.0  \n",
       "stp1_YPDRapa                   0.0           0.0  \n",
       "dal81_MinimalEtOH              0.0           0.0  \n",
       "...                            ...           ...  \n",
       "gcn4_YPDDiauxic                0.0           0.0  \n",
       "dal81_Urea                     0.0           0.0  \n",
       "rtg1_Glutamine                 0.0           0.0  \n",
       "rtg1_MinimalGlucose            0.0           0.0  \n",
       "dal82_YPD                      0.0           0.0  \n",
       "\n",
       "[132 rows x 275047 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# First we need to get a dataframe with samples on rows and edges on columns\n",
    "\n",
    "network.index = network.gene1 + '_' + network.gene2\n",
    "df = network.fillna(0).iloc[:,2:].T\n",
    "#df = network.dropna(axis = 0 ).iloc[:,2:].T\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sample</th>\n",
       "      <th>genetics</th>\n",
       "      <th>media</th>\n",
       "      <th>label_genetics</th>\n",
       "      <th>label_media</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>dal82_MinimalGlucose</td>\n",
       "      <td>dal82</td>\n",
       "      <td>MinimalGlucose</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>dal81_YPDDiauxic</td>\n",
       "      <td>dal81</td>\n",
       "      <td>YPDDiauxic</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>gln3_YPD</td>\n",
       "      <td>gln3</td>\n",
       "      <td>YPD</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>stp1_YPDRapa</td>\n",
       "      <td>stp1</td>\n",
       "      <td>YPDRapa</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>dal81_MinimalEtOH</td>\n",
       "      <td>dal81</td>\n",
       "      <td>MinimalEtOH</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>gcn4_YPDDiauxic</td>\n",
       "      <td>gcn4</td>\n",
       "      <td>YPDDiauxic</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>dal81_Urea</td>\n",
       "      <td>dal81</td>\n",
       "      <td>Urea</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>rtg1_Glutamine</td>\n",
       "      <td>rtg1</td>\n",
       "      <td>Glutamine</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>rtg1_MinimalGlucose</td>\n",
       "      <td>rtg1</td>\n",
       "      <td>MinimalGlucose</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>dal82_YPD</td>\n",
       "      <td>dal82</td>\n",
       "      <td>YPD</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>132 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   sample genetics           media  label_genetics  \\\n",
       "0    dal82_MinimalGlucose    dal82  MinimalGlucose               3   \n",
       "1        dal81_YPDDiauxic    dal81      YPDDiauxic               2   \n",
       "2                gln3_YPD     gln3             YPD               6   \n",
       "3            stp1_YPDRapa     stp1         YPDRapa              10   \n",
       "4       dal81_MinimalEtOH    dal81     MinimalEtOH               2   \n",
       "..                    ...      ...             ...             ...   \n",
       "127       gcn4_YPDDiauxic     gcn4      YPDDiauxic               5   \n",
       "128            dal81_Urea    dal81            Urea               2   \n",
       "129        rtg1_Glutamine     rtg1       Glutamine               8   \n",
       "130   rtg1_MinimalGlucose     rtg1  MinimalGlucose               8   \n",
       "131             dal82_YPD    dal82             YPD               3   \n",
       "\n",
       "     label_media  \n",
       "0              4  \n",
       "1              8  \n",
       "2              7  \n",
       "3              9  \n",
       "4              3  \n",
       "..           ...  \n",
       "127            8  \n",
       "128            6  \n",
       "129            2  \n",
       "130            4  \n",
       "131            7  \n",
       "\n",
       "[132 rows x 5 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We need to get a dataframe with the labels for each sample\n",
    "# genetics labels are labels between 0 and 11 (12 total)\n",
    "# media labels are labels between 0 and 10 (11 total)\n",
    "\n",
    "df_labels = pd.DataFrame(data = [df.index.tolist(), df.index.str.split('_').str[0].tolist(), df.index.str.split('_').str[1].tolist()], index = ['sample', 'genetics', 'media']).T\n",
    "df_labels['label_genetics'] = df_labels['genetics'].map({i[1]: i[0] for i in (df_labels.groupby(['genetics']).count().reset_index().sort_values(by = 'sample', ascending = False).iloc[:,:1]).itertuples()})\n",
    "df_labels['label_media'] = df_labels['media'].map({i[1]: i[0] for i in (df_labels.groupby(['media']).count().reset_index().sort_values(by = 'sample', ascending = False).iloc[:,:1]).itertuples()})\n",
    "#df_labels = df_labels.sort_values(by = 'label_media').set_index('sample')\n",
    "df_labels\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Clustering and performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select the number of clusters\n",
    "nc = 14"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sample</th>\n",
       "      <th>genetics</th>\n",
       "      <th>media</th>\n",
       "      <th>label_genetics</th>\n",
       "      <th>label_media</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>dal82_MinimalGlucose</td>\n",
       "      <td>dal82</td>\n",
       "      <td>MinimalGlucose</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>dal81_YPDDiauxic</td>\n",
       "      <td>dal81</td>\n",
       "      <td>YPDDiauxic</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>gln3_YPD</td>\n",
       "      <td>gln3</td>\n",
       "      <td>YPD</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>stp1_YPDRapa</td>\n",
       "      <td>stp1</td>\n",
       "      <td>YPDRapa</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>dal81_MinimalEtOH</td>\n",
       "      <td>dal81</td>\n",
       "      <td>MinimalEtOH</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>gcn4_YPDDiauxic</td>\n",
       "      <td>gcn4</td>\n",
       "      <td>YPDDiauxic</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>dal81_Urea</td>\n",
       "      <td>dal81</td>\n",
       "      <td>Urea</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>rtg1_Glutamine</td>\n",
       "      <td>rtg1</td>\n",
       "      <td>Glutamine</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>rtg1_MinimalGlucose</td>\n",
       "      <td>rtg1</td>\n",
       "      <td>MinimalGlucose</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>dal82_YPD</td>\n",
       "      <td>dal82</td>\n",
       "      <td>YPD</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>132 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   sample genetics           media  label_genetics  \\\n",
       "0    dal82_MinimalGlucose    dal82  MinimalGlucose               3   \n",
       "1        dal81_YPDDiauxic    dal81      YPDDiauxic               2   \n",
       "2                gln3_YPD     gln3             YPD               6   \n",
       "3            stp1_YPDRapa     stp1         YPDRapa              10   \n",
       "4       dal81_MinimalEtOH    dal81     MinimalEtOH               2   \n",
       "..                    ...      ...             ...             ...   \n",
       "127       gcn4_YPDDiauxic     gcn4      YPDDiauxic               5   \n",
       "128            dal81_Urea    dal81            Urea               2   \n",
       "129        rtg1_Glutamine     rtg1       Glutamine               8   \n",
       "130   rtg1_MinimalGlucose     rtg1  MinimalGlucose               8   \n",
       "131             dal82_YPD    dal82             YPD               3   \n",
       "\n",
       "     label_media  \n",
       "0              4  \n",
       "1              8  \n",
       "2              7  \n",
       "3              9  \n",
       "4              3  \n",
       "..           ...  \n",
       "127            8  \n",
       "128            6  \n",
       "129            2  \n",
       "130            4  \n",
       "131            7  \n",
       "\n",
       "[132 rows x 5 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_92391/557461149.py:26: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
      "  df_clustering = pd.concat([df_clustering, media_temp, genetics_temp], axis = 0, ignore_index=True)\n"
     ]
    }
   ],
   "source": [
    "# kmeans clustering\n",
    "from sklearn.cluster import KMeans\n",
    "est = KMeans(n_clusters=nc) # we pass two more clusters  \n",
    "est.fit(df)\n",
    "kmeans_labels = est.labels_\n",
    "df_kmeans = get_clustering_scores(df_labels['label_media'].values, df_labels['label_genetics'].values, kmeans_labels, method_label = 'kmeans')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/bonobo-env/lib/python3.9/site-packages/sklearn/manifold/_spectral_embedding.py:260: UserWarning: Graph is not fully connected, spectral embedding may not work as expected.\n",
      "  warnings.warn(\n",
      "/tmp/ipykernel_92391/557461149.py:26: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
      "  df_clustering = pd.concat([df_clustering, media_temp, genetics_temp], axis = 0, ignore_index=True)\n"
     ]
    }
   ],
   "source": [
    "# Spectral clustering\n",
    "from sklearn.cluster import SpectralClustering\n",
    "sc = SpectralClustering(nc,  n_init=10, assign_labels='discretize')\n",
    "sc.fit_predict(df)  \n",
    "\n",
    "df_spectral = get_clustering_scores(df_labels['label_media'].values, df_labels['label_genetics'].values, sc.labels_, method_label = 'spectral')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>metric</th>\n",
       "      <th>value</th>\n",
       "      <th>check</th>\n",
       "      <th>clustering_method</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>adjusted_rand_score</td>\n",
       "      <td>0.788710</td>\n",
       "      <td>media</td>\n",
       "      <td>spectral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>adjusted_rand_score</td>\n",
       "      <td>-0.061818</td>\n",
       "      <td>genetics</td>\n",
       "      <td>spectral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>adjusted_mutual_info_score</td>\n",
       "      <td>0.850745</td>\n",
       "      <td>media</td>\n",
       "      <td>spectral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>adjusted_mutual_info_score</td>\n",
       "      <td>-0.173302</td>\n",
       "      <td>genetics</td>\n",
       "      <td>spectral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>completeness_score</td>\n",
       "      <td>0.866380</td>\n",
       "      <td>media</td>\n",
       "      <td>spectral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>completeness_score</td>\n",
       "      <td>0.096266</td>\n",
       "      <td>genetics</td>\n",
       "      <td>spectral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>fowlkes_mallows_score</td>\n",
       "      <td>0.806273</td>\n",
       "      <td>media</td>\n",
       "      <td>spectral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>fowlkes_mallows_score</td>\n",
       "      <td>0.020806</td>\n",
       "      <td>genetics</td>\n",
       "      <td>spectral</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       metric     value     check clustering_method\n",
       "0         adjusted_rand_score  0.788710     media          spectral\n",
       "1         adjusted_rand_score -0.061818  genetics          spectral\n",
       "2  adjusted_mutual_info_score  0.850745     media          spectral\n",
       "3  adjusted_mutual_info_score -0.173302  genetics          spectral\n",
       "4          completeness_score  0.866380     media          spectral\n",
       "5          completeness_score  0.096266  genetics          spectral\n",
       "6       fowlkes_mallows_score  0.806273     media          spectral\n",
       "7       fowlkes_mallows_score  0.020806  genetics          spectral"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_spectral"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import DBSCAN\n",
    "\n",
    "if False:\n",
    "    sparse_corr = df.T.corr()\n",
    "\n",
    "    db = DBSCAN(eps=5, min_samples=2,metric= 'precomputed' ).fit(sparse_corr)\n",
    "    labels = db.labels_\n",
    "\n",
    "    # Number of clusters in labels, ignoring noise if present.\n",
    "    n_clusters_ = len(set(labels)) - (1 if -1 in labels else 0)\n",
    "    n_noise_ = list(labels).count(-1)\n",
    "\n",
    "    df_dbscan = get_clustering_scores(df_labels['label_media'].values, df_labels['label_genetics'].values, labels, method_label = 'dbscan')\n",
    "\n",
    "    print(n_clusters_, n_noise_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_92391/557461149.py:26: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
      "  df_clustering = pd.concat([df_clustering, media_temp, genetics_temp], axis = 0, ignore_index=True)\n"
     ]
    }
   ],
   "source": [
    "# Agglomerative clustering\n",
    "from sklearn.cluster import AgglomerativeClustering\n",
    "df_ac = pd.DataFrame()\n",
    "for link in ['ward']:\n",
    "    ac = AgglomerativeClustering(n_clusters=nc, linkage=link)\n",
    "    ac.fit_predict(df)  \n",
    "\n",
    "    df_ac_temp = get_clustering_scores(df_labels['label_media'].values, df_labels['label_genetics'].values, ac.labels_, method_label = 'ac_'+link)\n",
    "    df_ac = pd.concat([df_ac, df_ac_temp], axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Concatenate all tables and save\n",
    "if network_name.startswith('lioness'):\n",
    "    clustering_performance = pd.concat([df_kmeans, df_ac], axis = 0)\n",
    "else:\n",
    "    clustering_performance = pd.concat([df_kmeans, df_spectral, df_ac], axis = 0)\n",
    "clustering_performance.to_csv(results_folder + 'clustering_performance.csv', index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>metric</th>\n",
       "      <th>value</th>\n",
       "      <th>check</th>\n",
       "      <th>clustering_method</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>adjusted_rand_score</td>\n",
       "      <td>0.666824</td>\n",
       "      <td>media</td>\n",
       "      <td>kmeans</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>adjusted_rand_score</td>\n",
       "      <td>-0.057224</td>\n",
       "      <td>genetics</td>\n",
       "      <td>kmeans</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>adjusted_mutual_info_score</td>\n",
       "      <td>0.783423</td>\n",
       "      <td>media</td>\n",
       "      <td>kmeans</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>adjusted_mutual_info_score</td>\n",
       "      <td>-0.162483</td>\n",
       "      <td>genetics</td>\n",
       "      <td>kmeans</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>completeness_score</td>\n",
       "      <td>0.856780</td>\n",
       "      <td>media</td>\n",
       "      <td>kmeans</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>completeness_score</td>\n",
       "      <td>0.073394</td>\n",
       "      <td>genetics</td>\n",
       "      <td>kmeans</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>fowlkes_mallows_score</td>\n",
       "      <td>0.707943</td>\n",
       "      <td>media</td>\n",
       "      <td>kmeans</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>fowlkes_mallows_score</td>\n",
       "      <td>0.040702</td>\n",
       "      <td>genetics</td>\n",
       "      <td>kmeans</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>adjusted_rand_score</td>\n",
       "      <td>0.788710</td>\n",
       "      <td>media</td>\n",
       "      <td>spectral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>adjusted_rand_score</td>\n",
       "      <td>-0.061818</td>\n",
       "      <td>genetics</td>\n",
       "      <td>spectral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>adjusted_mutual_info_score</td>\n",
       "      <td>0.850745</td>\n",
       "      <td>media</td>\n",
       "      <td>spectral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>adjusted_mutual_info_score</td>\n",
       "      <td>-0.173302</td>\n",
       "      <td>genetics</td>\n",
       "      <td>spectral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>completeness_score</td>\n",
       "      <td>0.866380</td>\n",
       "      <td>media</td>\n",
       "      <td>spectral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>completeness_score</td>\n",
       "      <td>0.096266</td>\n",
       "      <td>genetics</td>\n",
       "      <td>spectral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>fowlkes_mallows_score</td>\n",
       "      <td>0.806273</td>\n",
       "      <td>media</td>\n",
       "      <td>spectral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>fowlkes_mallows_score</td>\n",
       "      <td>0.020806</td>\n",
       "      <td>genetics</td>\n",
       "      <td>spectral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>adjusted_rand_score</td>\n",
       "      <td>0.564483</td>\n",
       "      <td>media</td>\n",
       "      <td>ac_ward</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>adjusted_rand_score</td>\n",
       "      <td>-0.044679</td>\n",
       "      <td>genetics</td>\n",
       "      <td>ac_ward</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>adjusted_mutual_info_score</td>\n",
       "      <td>0.775324</td>\n",
       "      <td>media</td>\n",
       "      <td>ac_ward</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>adjusted_mutual_info_score</td>\n",
       "      <td>-0.149840</td>\n",
       "      <td>genetics</td>\n",
       "      <td>ac_ward</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>completeness_score</td>\n",
       "      <td>0.876451</td>\n",
       "      <td>media</td>\n",
       "      <td>ac_ward</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>completeness_score</td>\n",
       "      <td>0.088476</td>\n",
       "      <td>genetics</td>\n",
       "      <td>ac_ward</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>fowlkes_mallows_score</td>\n",
       "      <td>0.637702</td>\n",
       "      <td>media</td>\n",
       "      <td>ac_ward</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>fowlkes_mallows_score</td>\n",
       "      <td>0.064725</td>\n",
       "      <td>genetics</td>\n",
       "      <td>ac_ward</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       metric     value     check clustering_method\n",
       "0         adjusted_rand_score  0.666824     media            kmeans\n",
       "1         adjusted_rand_score -0.057224  genetics            kmeans\n",
       "2  adjusted_mutual_info_score  0.783423     media            kmeans\n",
       "3  adjusted_mutual_info_score -0.162483  genetics            kmeans\n",
       "4          completeness_score  0.856780     media            kmeans\n",
       "5          completeness_score  0.073394  genetics            kmeans\n",
       "6       fowlkes_mallows_score  0.707943     media            kmeans\n",
       "7       fowlkes_mallows_score  0.040702  genetics            kmeans\n",
       "0         adjusted_rand_score  0.788710     media          spectral\n",
       "1         adjusted_rand_score -0.061818  genetics          spectral\n",
       "2  adjusted_mutual_info_score  0.850745     media          spectral\n",
       "3  adjusted_mutual_info_score -0.173302  genetics          spectral\n",
       "4          completeness_score  0.866380     media          spectral\n",
       "5          completeness_score  0.096266  genetics          spectral\n",
       "6       fowlkes_mallows_score  0.806273     media          spectral\n",
       "7       fowlkes_mallows_score  0.020806  genetics          spectral\n",
       "0         adjusted_rand_score  0.564483     media           ac_ward\n",
       "1         adjusted_rand_score -0.044679  genetics           ac_ward\n",
       "2  adjusted_mutual_info_score  0.775324     media           ac_ward\n",
       "3  adjusted_mutual_info_score -0.149840  genetics           ac_ward\n",
       "4          completeness_score  0.876451     media           ac_ward\n",
       "5          completeness_score  0.088476  genetics           ac_ward\n",
       "6       fowlkes_mallows_score  0.637702     media           ac_ward\n",
       "7       fowlkes_mallows_score  0.064725  genetics           ac_ward"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clustering_performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 995.875x600 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_context('paper')\n",
    "g1 = sns.catplot(data = clustering_performance.reset_index(), \n",
    "            col = 'metric', \n",
    "            y = 'value', \n",
    "            hue = 'check',\n",
    "            x = 'clustering_method', \n",
    "            kind = 'bar', \n",
    "            aspect = 1.5,\n",
    "            height=3, \n",
    "            col_wrap = 2,\n",
    "            sharey = False, \n",
    "            hue_order = ['media', 'genetics'],\n",
    "            palette = [media_color, genetics_color], )\n",
    "\n",
    "# add title\n",
    "g1.fig.suptitle(network_name)\n",
    "plt.tight_layout()\n",
    "\n",
    "# Save figure\n",
    "g1.savefig(results_folder + 'clustering_performance.pdf', bbox_inches = 'tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extra\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gene1</th>\n",
       "      <th>gene2</th>\n",
       "      <th>dal81_AmmoniumSulfate</th>\n",
       "      <th>dal81_CStarve</th>\n",
       "      <th>dal81_Glutamine</th>\n",
       "      <th>dal81_MinimalEtOH</th>\n",
       "      <th>dal81_MinimalGlucose</th>\n",
       "      <th>dal81_Proline</th>\n",
       "      <th>dal81_Urea</th>\n",
       "      <th>dal81_YPD</th>\n",
       "      <th>...</th>\n",
       "      <th>gzf3_CStarve</th>\n",
       "      <th>gzf3_Glutamine</th>\n",
       "      <th>gzf3_MinimalEtOH</th>\n",
       "      <th>gzf3_MinimalGlucose</th>\n",
       "      <th>gzf3_Proline</th>\n",
       "      <th>gzf3_Urea</th>\n",
       "      <th>gzf3_YPD</th>\n",
       "      <th>gzf3_YPDDiauxic</th>\n",
       "      <th>gzf3_YPDRapa</th>\n",
       "      <th>gzf3_YPEtOH</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>YBR093C</td>\n",
       "      <td>YAR071W</td>\n",
       "      <td>-0.000012</td>\n",
       "      <td>0.011596</td>\n",
       "      <td>0.000796</td>\n",
       "      <td>0.001114</td>\n",
       "      <td>0.000197</td>\n",
       "      <td>0.002902</td>\n",
       "      <td>0.003674</td>\n",
       "      <td>0.238742</td>\n",
       "      <td>...</td>\n",
       "      <td>0.004535</td>\n",
       "      <td>-0.000906</td>\n",
       "      <td>0.000253</td>\n",
       "      <td>0.013554</td>\n",
       "      <td>0.005389</td>\n",
       "      <td>-0.007587</td>\n",
       "      <td>0.073221</td>\n",
       "      <td>0.018696</td>\n",
       "      <td>0.000068</td>\n",
       "      <td>-0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>YLR413W</td>\n",
       "      <td>YBR093C</td>\n",
       "      <td>-0.000095</td>\n",
       "      <td>0.008875</td>\n",
       "      <td>-0.000123</td>\n",
       "      <td>0.001146</td>\n",
       "      <td>0.000237</td>\n",
       "      <td>0.003097</td>\n",
       "      <td>0.004737</td>\n",
       "      <td>0.278228</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003697</td>\n",
       "      <td>-0.000115</td>\n",
       "      <td>0.000327</td>\n",
       "      <td>-0.002040</td>\n",
       "      <td>0.003864</td>\n",
       "      <td>-0.000732</td>\n",
       "      <td>0.067399</td>\n",
       "      <td>0.018746</td>\n",
       "      <td>0.000096</td>\n",
       "      <td>-0.000656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>YHR215W</td>\n",
       "      <td>YAR071W</td>\n",
       "      <td>0.000029</td>\n",
       "      <td>0.012201</td>\n",
       "      <td>0.003357</td>\n",
       "      <td>0.002399</td>\n",
       "      <td>-0.000579</td>\n",
       "      <td>0.004781</td>\n",
       "      <td>0.002572</td>\n",
       "      <td>0.246591</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005073</td>\n",
       "      <td>0.000373</td>\n",
       "      <td>0.000778</td>\n",
       "      <td>0.016841</td>\n",
       "      <td>0.006661</td>\n",
       "      <td>0.042773</td>\n",
       "      <td>0.049321</td>\n",
       "      <td>0.031833</td>\n",
       "      <td>0.000713</td>\n",
       "      <td>-0.000005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>YLR413W</td>\n",
       "      <td>YBR092C</td>\n",
       "      <td>0.000036</td>\n",
       "      <td>0.006613</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>0.001546</td>\n",
       "      <td>-0.000233</td>\n",
       "      <td>0.000598</td>\n",
       "      <td>-0.003806</td>\n",
       "      <td>0.571926</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003317</td>\n",
       "      <td>0.000083</td>\n",
       "      <td>0.000986</td>\n",
       "      <td>0.000874</td>\n",
       "      <td>0.003003</td>\n",
       "      <td>-0.001001</td>\n",
       "      <td>0.058173</td>\n",
       "      <td>0.021117</td>\n",
       "      <td>-0.000972</td>\n",
       "      <td>0.000523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>YBR093C</td>\n",
       "      <td>YHR215W</td>\n",
       "      <td>-0.000260</td>\n",
       "      <td>0.012187</td>\n",
       "      <td>0.001013</td>\n",
       "      <td>0.001260</td>\n",
       "      <td>-0.000480</td>\n",
       "      <td>0.003908</td>\n",
       "      <td>0.007048</td>\n",
       "      <td>0.218182</td>\n",
       "      <td>...</td>\n",
       "      <td>0.004854</td>\n",
       "      <td>-0.000744</td>\n",
       "      <td>0.001551</td>\n",
       "      <td>0.017854</td>\n",
       "      <td>0.005476</td>\n",
       "      <td>-0.008207</td>\n",
       "      <td>0.063263</td>\n",
       "      <td>0.019714</td>\n",
       "      <td>0.000217</td>\n",
       "      <td>0.000260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>YLR413W</td>\n",
       "      <td>YPR036W.A</td>\n",
       "      <td>0.000139</td>\n",
       "      <td>0.009349</td>\n",
       "      <td>0.000877</td>\n",
       "      <td>-0.003568</td>\n",
       "      <td>-0.000121</td>\n",
       "      <td>-0.005225</td>\n",
       "      <td>0.008626</td>\n",
       "      <td>-0.244071</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003692</td>\n",
       "      <td>-0.000207</td>\n",
       "      <td>-0.001891</td>\n",
       "      <td>0.000239</td>\n",
       "      <td>-0.005818</td>\n",
       "      <td>0.000186</td>\n",
       "      <td>-0.040909</td>\n",
       "      <td>-0.039115</td>\n",
       "      <td>0.000049</td>\n",
       "      <td>-0.001209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>YLR413W</td>\n",
       "      <td>YPR149W</td>\n",
       "      <td>0.000108</td>\n",
       "      <td>-0.009459</td>\n",
       "      <td>0.000800</td>\n",
       "      <td>-0.003258</td>\n",
       "      <td>0.000137</td>\n",
       "      <td>-0.001312</td>\n",
       "      <td>-0.000046</td>\n",
       "      <td>-0.241024</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.004849</td>\n",
       "      <td>0.000054</td>\n",
       "      <td>-0.001993</td>\n",
       "      <td>0.000578</td>\n",
       "      <td>-0.002003</td>\n",
       "      <td>-0.000317</td>\n",
       "      <td>-0.041856</td>\n",
       "      <td>0.001212</td>\n",
       "      <td>-0.003195</td>\n",
       "      <td>0.000261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>YMR251W.A</td>\n",
       "      <td>YBR093C</td>\n",
       "      <td>-0.000139</td>\n",
       "      <td>0.015429</td>\n",
       "      <td>-0.002134</td>\n",
       "      <td>0.000054</td>\n",
       "      <td>-0.000091</td>\n",
       "      <td>-0.003352</td>\n",
       "      <td>-0.001355</td>\n",
       "      <td>-0.256765</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005976</td>\n",
       "      <td>0.005280</td>\n",
       "      <td>-0.001222</td>\n",
       "      <td>0.005584</td>\n",
       "      <td>-0.005196</td>\n",
       "      <td>-0.001876</td>\n",
       "      <td>-0.070767</td>\n",
       "      <td>0.005922</td>\n",
       "      <td>0.000128</td>\n",
       "      <td>0.000242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>YMR251W.A</td>\n",
       "      <td>YBR092C</td>\n",
       "      <td>0.000054</td>\n",
       "      <td>0.011496</td>\n",
       "      <td>0.000100</td>\n",
       "      <td>0.000073</td>\n",
       "      <td>0.000089</td>\n",
       "      <td>-0.000647</td>\n",
       "      <td>0.001089</td>\n",
       "      <td>-0.527807</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005362</td>\n",
       "      <td>-0.003818</td>\n",
       "      <td>-0.003682</td>\n",
       "      <td>-0.002393</td>\n",
       "      <td>-0.004039</td>\n",
       "      <td>-0.002566</td>\n",
       "      <td>-0.061080</td>\n",
       "      <td>0.006671</td>\n",
       "      <td>-0.001304</td>\n",
       "      <td>-0.000193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>YLR413W</td>\n",
       "      <td>YMR251W.A</td>\n",
       "      <td>0.000120</td>\n",
       "      <td>0.011821</td>\n",
       "      <td>0.001089</td>\n",
       "      <td>0.000106</td>\n",
       "      <td>-0.000132</td>\n",
       "      <td>-0.004375</td>\n",
       "      <td>-0.000638</td>\n",
       "      <td>-0.338195</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005092</td>\n",
       "      <td>-0.000335</td>\n",
       "      <td>-0.000794</td>\n",
       "      <td>-0.000793</td>\n",
       "      <td>-0.004532</td>\n",
       "      <td>0.000943</td>\n",
       "      <td>-0.050784</td>\n",
       "      <td>0.009587</td>\n",
       "      <td>0.000592</td>\n",
       "      <td>-0.001010</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 134 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         gene1      gene2  dal81_AmmoniumSulfate  dal81_CStarve  \\\n",
       "0      YBR093C    YAR071W              -0.000012       0.011596   \n",
       "1      YLR413W    YBR093C              -0.000095       0.008875   \n",
       "2      YHR215W    YAR071W               0.000029       0.012201   \n",
       "3      YLR413W    YBR092C               0.000036       0.006613   \n",
       "4      YBR093C    YHR215W              -0.000260       0.012187   \n",
       "..         ...        ...                    ...            ...   \n",
       "995    YLR413W  YPR036W.A               0.000139       0.009349   \n",
       "996    YLR413W    YPR149W               0.000108      -0.009459   \n",
       "997  YMR251W.A    YBR093C              -0.000139       0.015429   \n",
       "998  YMR251W.A    YBR092C               0.000054       0.011496   \n",
       "999    YLR413W  YMR251W.A               0.000120       0.011821   \n",
       "\n",
       "     dal81_Glutamine  dal81_MinimalEtOH  dal81_MinimalGlucose  dal81_Proline  \\\n",
       "0           0.000796           0.001114              0.000197       0.002902   \n",
       "1          -0.000123           0.001146              0.000237       0.003097   \n",
       "2           0.003357           0.002399             -0.000579       0.004781   \n",
       "3           0.000006           0.001546             -0.000233       0.000598   \n",
       "4           0.001013           0.001260             -0.000480       0.003908   \n",
       "..               ...                ...                   ...            ...   \n",
       "995         0.000877          -0.003568             -0.000121      -0.005225   \n",
       "996         0.000800          -0.003258              0.000137      -0.001312   \n",
       "997        -0.002134           0.000054             -0.000091      -0.003352   \n",
       "998         0.000100           0.000073              0.000089      -0.000647   \n",
       "999         0.001089           0.000106             -0.000132      -0.004375   \n",
       "\n",
       "     dal81_Urea  dal81_YPD  ...  gzf3_CStarve  gzf3_Glutamine  \\\n",
       "0      0.003674   0.238742  ...      0.004535       -0.000906   \n",
       "1      0.004737   0.278228  ...      0.003697       -0.000115   \n",
       "2      0.002572   0.246591  ...      0.005073        0.000373   \n",
       "3     -0.003806   0.571926  ...      0.003317        0.000083   \n",
       "4      0.007048   0.218182  ...      0.004854       -0.000744   \n",
       "..          ...        ...  ...           ...             ...   \n",
       "995    0.008626  -0.244071  ...      0.003692       -0.000207   \n",
       "996   -0.000046  -0.241024  ...     -0.004849        0.000054   \n",
       "997   -0.001355  -0.256765  ...      0.005976        0.005280   \n",
       "998    0.001089  -0.527807  ...      0.005362       -0.003818   \n",
       "999   -0.000638  -0.338195  ...      0.005092       -0.000335   \n",
       "\n",
       "     gzf3_MinimalEtOH  gzf3_MinimalGlucose  gzf3_Proline  gzf3_Urea  gzf3_YPD  \\\n",
       "0            0.000253             0.013554      0.005389  -0.007587  0.073221   \n",
       "1            0.000327            -0.002040      0.003864  -0.000732  0.067399   \n",
       "2            0.000778             0.016841      0.006661   0.042773  0.049321   \n",
       "3            0.000986             0.000874      0.003003  -0.001001  0.058173   \n",
       "4            0.001551             0.017854      0.005476  -0.008207  0.063263   \n",
       "..                ...                  ...           ...        ...       ...   \n",
       "995         -0.001891             0.000239     -0.005818   0.000186 -0.040909   \n",
       "996         -0.001993             0.000578     -0.002003  -0.000317 -0.041856   \n",
       "997         -0.001222             0.005584     -0.005196  -0.001876 -0.070767   \n",
       "998         -0.003682            -0.002393     -0.004039  -0.002566 -0.061080   \n",
       "999         -0.000794            -0.000793     -0.004532   0.000943 -0.050784   \n",
       "\n",
       "     gzf3_YPDDiauxic  gzf3_YPDRapa  gzf3_YPEtOH  \n",
       "0           0.018696      0.000068    -0.000003  \n",
       "1           0.018746      0.000096    -0.000656  \n",
       "2           0.031833      0.000713    -0.000005  \n",
       "3           0.021117     -0.000972     0.000523  \n",
       "4           0.019714      0.000217     0.000260  \n",
       "..               ...           ...          ...  \n",
       "995        -0.039115      0.000049    -0.001209  \n",
       "996         0.001212     -0.003195     0.000261  \n",
       "997         0.005922      0.000128     0.000242  \n",
       "998         0.006671     -0.001304    -0.000193  \n",
       "999         0.009587      0.000592    -0.001010  \n",
       "\n",
       "[1000 rows x 134 columns]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "old_spcc = pd.read_csv('../results/pseudobulk_nonzero/old_spcc_top1k.csv')\n",
    "old_spcc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gene1</th>\n",
       "      <th>gene2</th>\n",
       "      <th>dal81_AmmoniumSulfate</th>\n",
       "      <th>dal81_CStarve</th>\n",
       "      <th>dal81_Glutamine</th>\n",
       "      <th>dal81_MinimalEtOH</th>\n",
       "      <th>dal81_MinimalGlucose</th>\n",
       "      <th>dal81_Proline</th>\n",
       "      <th>dal81_Urea</th>\n",
       "      <th>dal81_YPD</th>\n",
       "      <th>...</th>\n",
       "      <th>gzf3_CStarve</th>\n",
       "      <th>gzf3_Glutamine</th>\n",
       "      <th>gzf3_MinimalEtOH</th>\n",
       "      <th>gzf3_MinimalGlucose</th>\n",
       "      <th>gzf3_Proline</th>\n",
       "      <th>gzf3_Urea</th>\n",
       "      <th>gzf3_YPD</th>\n",
       "      <th>gzf3_YPDDiauxic</th>\n",
       "      <th>gzf3_YPDRapa</th>\n",
       "      <th>gzf3_YPEtOH</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>YOL077C</td>\n",
       "      <td>YEL026W</td>\n",
       "      <td>0.000792</td>\n",
       "      <td>0.02406</td>\n",
       "      <td>0.004925</td>\n",
       "      <td>-0.000734</td>\n",
       "      <td>0.013987</td>\n",
       "      <td>0.000721</td>\n",
       "      <td>0.011158</td>\n",
       "      <td>0.119866</td>\n",
       "      <td>...</td>\n",
       "      <td>0.010543</td>\n",
       "      <td>0.002456</td>\n",
       "      <td>0.001684</td>\n",
       "      <td>0.007231</td>\n",
       "      <td>0.001427</td>\n",
       "      <td>-0.000286</td>\n",
       "      <td>0.028653</td>\n",
       "      <td>0.102039</td>\n",
       "      <td>0.010537</td>\n",
       "      <td>0.002198</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 134 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       gene1    gene2  dal81_AmmoniumSulfate  dal81_CStarve  dal81_Glutamine  \\\n",
       "237  YOL077C  YEL026W               0.000792        0.02406         0.004925   \n",
       "\n",
       "     dal81_MinimalEtOH  dal81_MinimalGlucose  dal81_Proline  dal81_Urea  \\\n",
       "237          -0.000734              0.013987       0.000721    0.011158   \n",
       "\n",
       "     dal81_YPD  ...  gzf3_CStarve  gzf3_Glutamine  gzf3_MinimalEtOH  \\\n",
       "237   0.119866  ...      0.010543        0.002456          0.001684   \n",
       "\n",
       "     gzf3_MinimalGlucose  gzf3_Proline  gzf3_Urea  gzf3_YPD  gzf3_YPDDiauxic  \\\n",
       "237             0.007231      0.001427  -0.000286  0.028653         0.102039   \n",
       "\n",
       "     gzf3_YPDRapa  gzf3_YPEtOH  \n",
       "237      0.010537     0.002198  \n",
       "\n",
       "[1 rows x 134 columns]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "old_spcc[(old_spcc['gene1']=='YOL077C') & (old_spcc['gene2']=='YEL026W')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gene1</th>\n",
       "      <th>gene2</th>\n",
       "      <th>dal81_AmmoniumSulfate</th>\n",
       "      <th>dal81_CStarve</th>\n",
       "      <th>dal81_Glutamine</th>\n",
       "      <th>dal81_MinimalEtOH</th>\n",
       "      <th>dal81_MinimalGlucose</th>\n",
       "      <th>dal81_Proline</th>\n",
       "      <th>dal81_Urea</th>\n",
       "      <th>dal81_YPD</th>\n",
       "      <th>...</th>\n",
       "      <th>gzf3_CStarve</th>\n",
       "      <th>gzf3_Glutamine</th>\n",
       "      <th>gzf3_MinimalEtOH</th>\n",
       "      <th>gzf3_MinimalGlucose</th>\n",
       "      <th>gzf3_Proline</th>\n",
       "      <th>gzf3_Urea</th>\n",
       "      <th>gzf3_YPD</th>\n",
       "      <th>gzf3_YPDDiauxic</th>\n",
       "      <th>gzf3_YPDRapa</th>\n",
       "      <th>gzf3_YPEtOH</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>YBR093C</td>\n",
       "      <td>YAR071W</td>\n",
       "      <td>-0.000012</td>\n",
       "      <td>0.011596</td>\n",
       "      <td>0.000796</td>\n",
       "      <td>0.001114</td>\n",
       "      <td>0.000197</td>\n",
       "      <td>0.002902</td>\n",
       "      <td>0.003674</td>\n",
       "      <td>0.238742</td>\n",
       "      <td>...</td>\n",
       "      <td>0.004535</td>\n",
       "      <td>-0.000906</td>\n",
       "      <td>0.000253</td>\n",
       "      <td>0.013554</td>\n",
       "      <td>0.005389</td>\n",
       "      <td>-0.007587</td>\n",
       "      <td>0.073221</td>\n",
       "      <td>0.018696</td>\n",
       "      <td>0.000068</td>\n",
       "      <td>-0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>YLR413W</td>\n",
       "      <td>YBR093C</td>\n",
       "      <td>-0.000095</td>\n",
       "      <td>0.008875</td>\n",
       "      <td>-0.000123</td>\n",
       "      <td>0.001146</td>\n",
       "      <td>0.000237</td>\n",
       "      <td>0.003097</td>\n",
       "      <td>0.004737</td>\n",
       "      <td>0.278228</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003697</td>\n",
       "      <td>-0.000115</td>\n",
       "      <td>0.000327</td>\n",
       "      <td>-0.002040</td>\n",
       "      <td>0.003864</td>\n",
       "      <td>-0.000732</td>\n",
       "      <td>0.067399</td>\n",
       "      <td>0.018746</td>\n",
       "      <td>0.000096</td>\n",
       "      <td>-0.000656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>YHR215W</td>\n",
       "      <td>YAR071W</td>\n",
       "      <td>0.000029</td>\n",
       "      <td>0.012201</td>\n",
       "      <td>0.003357</td>\n",
       "      <td>0.002399</td>\n",
       "      <td>-0.000579</td>\n",
       "      <td>0.004781</td>\n",
       "      <td>0.002572</td>\n",
       "      <td>0.246591</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005073</td>\n",
       "      <td>0.000373</td>\n",
       "      <td>0.000778</td>\n",
       "      <td>0.016841</td>\n",
       "      <td>0.006661</td>\n",
       "      <td>0.042773</td>\n",
       "      <td>0.049321</td>\n",
       "      <td>0.031833</td>\n",
       "      <td>0.000713</td>\n",
       "      <td>-0.000005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>YLR413W</td>\n",
       "      <td>YBR092C</td>\n",
       "      <td>0.000036</td>\n",
       "      <td>0.006613</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>0.001546</td>\n",
       "      <td>-0.000233</td>\n",
       "      <td>0.000598</td>\n",
       "      <td>-0.003806</td>\n",
       "      <td>0.571926</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003317</td>\n",
       "      <td>0.000083</td>\n",
       "      <td>0.000986</td>\n",
       "      <td>0.000874</td>\n",
       "      <td>0.003003</td>\n",
       "      <td>-0.001001</td>\n",
       "      <td>0.058173</td>\n",
       "      <td>0.021117</td>\n",
       "      <td>-0.000972</td>\n",
       "      <td>0.000523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>YBR093C</td>\n",
       "      <td>YHR215W</td>\n",
       "      <td>-0.000260</td>\n",
       "      <td>0.012187</td>\n",
       "      <td>0.001013</td>\n",
       "      <td>0.001260</td>\n",
       "      <td>-0.000480</td>\n",
       "      <td>0.003908</td>\n",
       "      <td>0.007048</td>\n",
       "      <td>0.218182</td>\n",
       "      <td>...</td>\n",
       "      <td>0.004854</td>\n",
       "      <td>-0.000744</td>\n",
       "      <td>0.001551</td>\n",
       "      <td>0.017854</td>\n",
       "      <td>0.005476</td>\n",
       "      <td>-0.008207</td>\n",
       "      <td>0.063263</td>\n",
       "      <td>0.019714</td>\n",
       "      <td>0.000217</td>\n",
       "      <td>0.000260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>YMR251W.A</td>\n",
       "      <td>YBR093C</td>\n",
       "      <td>-0.000139</td>\n",
       "      <td>0.015429</td>\n",
       "      <td>-0.002134</td>\n",
       "      <td>0.000054</td>\n",
       "      <td>-0.000091</td>\n",
       "      <td>-0.003352</td>\n",
       "      <td>-0.001355</td>\n",
       "      <td>-0.256765</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005976</td>\n",
       "      <td>0.005280</td>\n",
       "      <td>-0.001222</td>\n",
       "      <td>0.005584</td>\n",
       "      <td>-0.005196</td>\n",
       "      <td>-0.001876</td>\n",
       "      <td>-0.070767</td>\n",
       "      <td>0.005922</td>\n",
       "      <td>0.000128</td>\n",
       "      <td>0.000242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>YLR413W</td>\n",
       "      <td>YPR036W.A</td>\n",
       "      <td>0.000139</td>\n",
       "      <td>0.009349</td>\n",
       "      <td>0.000877</td>\n",
       "      <td>-0.003568</td>\n",
       "      <td>-0.000121</td>\n",
       "      <td>-0.005225</td>\n",
       "      <td>0.008626</td>\n",
       "      <td>-0.244071</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003692</td>\n",
       "      <td>-0.000207</td>\n",
       "      <td>-0.001891</td>\n",
       "      <td>0.000239</td>\n",
       "      <td>-0.005818</td>\n",
       "      <td>0.000186</td>\n",
       "      <td>-0.040909</td>\n",
       "      <td>-0.039115</td>\n",
       "      <td>0.000049</td>\n",
       "      <td>-0.001209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>YMR251W.A</td>\n",
       "      <td>YBR092C</td>\n",
       "      <td>0.000054</td>\n",
       "      <td>0.011496</td>\n",
       "      <td>0.000100</td>\n",
       "      <td>0.000073</td>\n",
       "      <td>0.000089</td>\n",
       "      <td>-0.000647</td>\n",
       "      <td>0.001089</td>\n",
       "      <td>-0.527807</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005362</td>\n",
       "      <td>-0.003818</td>\n",
       "      <td>-0.003682</td>\n",
       "      <td>-0.002393</td>\n",
       "      <td>-0.004039</td>\n",
       "      <td>-0.002566</td>\n",
       "      <td>-0.061080</td>\n",
       "      <td>0.006671</td>\n",
       "      <td>-0.001304</td>\n",
       "      <td>-0.000193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>YLR413W</td>\n",
       "      <td>YPR149W</td>\n",
       "      <td>0.000108</td>\n",
       "      <td>-0.009459</td>\n",
       "      <td>0.000800</td>\n",
       "      <td>-0.003258</td>\n",
       "      <td>0.000137</td>\n",
       "      <td>-0.001312</td>\n",
       "      <td>-0.000046</td>\n",
       "      <td>-0.241024</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.004849</td>\n",
       "      <td>0.000054</td>\n",
       "      <td>-0.001993</td>\n",
       "      <td>0.000578</td>\n",
       "      <td>-0.002003</td>\n",
       "      <td>-0.000317</td>\n",
       "      <td>-0.041856</td>\n",
       "      <td>0.001212</td>\n",
       "      <td>-0.003195</td>\n",
       "      <td>0.000261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>YLR413W</td>\n",
       "      <td>YMR251W.A</td>\n",
       "      <td>0.000120</td>\n",
       "      <td>0.011821</td>\n",
       "      <td>0.001089</td>\n",
       "      <td>0.000106</td>\n",
       "      <td>-0.000132</td>\n",
       "      <td>-0.004375</td>\n",
       "      <td>-0.000638</td>\n",
       "      <td>-0.338195</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005092</td>\n",
       "      <td>-0.000335</td>\n",
       "      <td>-0.000794</td>\n",
       "      <td>-0.000793</td>\n",
       "      <td>-0.004532</td>\n",
       "      <td>0.000943</td>\n",
       "      <td>-0.050784</td>\n",
       "      <td>0.009587</td>\n",
       "      <td>0.000592</td>\n",
       "      <td>-0.001010</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 134 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         gene1      gene2  dal81_AmmoniumSulfate  dal81_CStarve  \\\n",
       "0      YBR093C    YAR071W              -0.000012       0.011596   \n",
       "1      YLR413W    YBR093C              -0.000095       0.008875   \n",
       "2      YHR215W    YAR071W               0.000029       0.012201   \n",
       "3      YLR413W    YBR092C               0.000036       0.006613   \n",
       "4      YBR093C    YHR215W              -0.000260       0.012187   \n",
       "..         ...        ...                    ...            ...   \n",
       "995  YMR251W.A    YBR093C              -0.000139       0.015429   \n",
       "996    YLR413W  YPR036W.A               0.000139       0.009349   \n",
       "997  YMR251W.A    YBR092C               0.000054       0.011496   \n",
       "998    YLR413W    YPR149W               0.000108      -0.009459   \n",
       "999    YLR413W  YMR251W.A               0.000120       0.011821   \n",
       "\n",
       "     dal81_Glutamine  dal81_MinimalEtOH  dal81_MinimalGlucose  dal81_Proline  \\\n",
       "0           0.000796           0.001114              0.000197       0.002902   \n",
       "1          -0.000123           0.001146              0.000237       0.003097   \n",
       "2           0.003357           0.002399             -0.000579       0.004781   \n",
       "3           0.000006           0.001546             -0.000233       0.000598   \n",
       "4           0.001013           0.001260             -0.000480       0.003908   \n",
       "..               ...                ...                   ...            ...   \n",
       "995        -0.002134           0.000054             -0.000091      -0.003352   \n",
       "996         0.000877          -0.003568             -0.000121      -0.005225   \n",
       "997         0.000100           0.000073              0.000089      -0.000647   \n",
       "998         0.000800          -0.003258              0.000137      -0.001312   \n",
       "999         0.001089           0.000106             -0.000132      -0.004375   \n",
       "\n",
       "     dal81_Urea  dal81_YPD  ...  gzf3_CStarve  gzf3_Glutamine  \\\n",
       "0      0.003674   0.238742  ...      0.004535       -0.000906   \n",
       "1      0.004737   0.278228  ...      0.003697       -0.000115   \n",
       "2      0.002572   0.246591  ...      0.005073        0.000373   \n",
       "3     -0.003806   0.571926  ...      0.003317        0.000083   \n",
       "4      0.007048   0.218182  ...      0.004854       -0.000744   \n",
       "..          ...        ...  ...           ...             ...   \n",
       "995   -0.001355  -0.256765  ...      0.005976        0.005280   \n",
       "996    0.008626  -0.244071  ...      0.003692       -0.000207   \n",
       "997    0.001089  -0.527807  ...      0.005362       -0.003818   \n",
       "998   -0.000046  -0.241024  ...     -0.004849        0.000054   \n",
       "999   -0.000638  -0.338195  ...      0.005092       -0.000335   \n",
       "\n",
       "     gzf3_MinimalEtOH  gzf3_MinimalGlucose  gzf3_Proline  gzf3_Urea  gzf3_YPD  \\\n",
       "0            0.000253             0.013554      0.005389  -0.007587  0.073221   \n",
       "1            0.000327            -0.002040      0.003864  -0.000732  0.067399   \n",
       "2            0.000778             0.016841      0.006661   0.042773  0.049321   \n",
       "3            0.000986             0.000874      0.003003  -0.001001  0.058173   \n",
       "4            0.001551             0.017854      0.005476  -0.008207  0.063263   \n",
       "..                ...                  ...           ...        ...       ...   \n",
       "995         -0.001222             0.005584     -0.005196  -0.001876 -0.070767   \n",
       "996         -0.001891             0.000239     -0.005818   0.000186 -0.040909   \n",
       "997         -0.003682            -0.002393     -0.004039  -0.002566 -0.061080   \n",
       "998         -0.001993             0.000578     -0.002003  -0.000317 -0.041856   \n",
       "999         -0.000794            -0.000793     -0.004532   0.000943 -0.050784   \n",
       "\n",
       "     gzf3_YPDDiauxic  gzf3_YPDRapa  gzf3_YPEtOH  \n",
       "0           0.018696      0.000068    -0.000003  \n",
       "1           0.018746      0.000096    -0.000656  \n",
       "2           0.031833      0.000713    -0.000005  \n",
       "3           0.021117     -0.000972     0.000523  \n",
       "4           0.019714      0.000217     0.000260  \n",
       "..               ...           ...          ...  \n",
       "995         0.005922      0.000128     0.000242  \n",
       "996        -0.039115      0.000049    -0.001209  \n",
       "997         0.006671     -0.001304    -0.000193  \n",
       "998         0.001212     -0.003195     0.000261  \n",
       "999         0.009587      0.000592    -0.001010  \n",
       "\n",
       "[1000 rows x 134 columns]"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_spcc = pd.read_csv('../results/pseudobulk_nonzero/networks/inferelator_spcc_top1k.csv')\n",
    "new_spcc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gene1</th>\n",
       "      <th>gene2</th>\n",
       "      <th>dal81_AmmoniumSulfate</th>\n",
       "      <th>dal81_CStarve</th>\n",
       "      <th>dal81_Glutamine</th>\n",
       "      <th>dal81_MinimalEtOH</th>\n",
       "      <th>dal81_MinimalGlucose</th>\n",
       "      <th>dal81_Proline</th>\n",
       "      <th>dal81_Urea</th>\n",
       "      <th>dal81_YPD</th>\n",
       "      <th>...</th>\n",
       "      <th>gzf3_CStarve</th>\n",
       "      <th>gzf3_Glutamine</th>\n",
       "      <th>gzf3_MinimalEtOH</th>\n",
       "      <th>gzf3_MinimalGlucose</th>\n",
       "      <th>gzf3_Proline</th>\n",
       "      <th>gzf3_Urea</th>\n",
       "      <th>gzf3_YPD</th>\n",
       "      <th>gzf3_YPDDiauxic</th>\n",
       "      <th>gzf3_YPDRapa</th>\n",
       "      <th>gzf3_YPEtOH</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>YBR093C</td>\n",
       "      <td>YAR071W</td>\n",
       "      <td>-0.000012</td>\n",
       "      <td>0.011596</td>\n",
       "      <td>0.000796</td>\n",
       "      <td>0.001114</td>\n",
       "      <td>0.000197</td>\n",
       "      <td>0.002902</td>\n",
       "      <td>0.003674</td>\n",
       "      <td>0.238742</td>\n",
       "      <td>...</td>\n",
       "      <td>0.004535</td>\n",
       "      <td>-0.000906</td>\n",
       "      <td>0.000253</td>\n",
       "      <td>0.013554</td>\n",
       "      <td>0.005389</td>\n",
       "      <td>-0.007587</td>\n",
       "      <td>0.073221</td>\n",
       "      <td>0.018696</td>\n",
       "      <td>0.000068</td>\n",
       "      <td>-0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>YLR413W</td>\n",
       "      <td>YBR093C</td>\n",
       "      <td>-0.000095</td>\n",
       "      <td>0.008875</td>\n",
       "      <td>-0.000123</td>\n",
       "      <td>0.001146</td>\n",
       "      <td>0.000237</td>\n",
       "      <td>0.003097</td>\n",
       "      <td>0.004737</td>\n",
       "      <td>0.278228</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003697</td>\n",
       "      <td>-0.000115</td>\n",
       "      <td>0.000327</td>\n",
       "      <td>-0.002040</td>\n",
       "      <td>0.003864</td>\n",
       "      <td>-0.000732</td>\n",
       "      <td>0.067399</td>\n",
       "      <td>0.018746</td>\n",
       "      <td>0.000096</td>\n",
       "      <td>-0.000656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>YHR215W</td>\n",
       "      <td>YAR071W</td>\n",
       "      <td>0.000029</td>\n",
       "      <td>0.012201</td>\n",
       "      <td>0.003357</td>\n",
       "      <td>0.002399</td>\n",
       "      <td>-0.000579</td>\n",
       "      <td>0.004781</td>\n",
       "      <td>0.002572</td>\n",
       "      <td>0.246591</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005073</td>\n",
       "      <td>0.000373</td>\n",
       "      <td>0.000778</td>\n",
       "      <td>0.016841</td>\n",
       "      <td>0.006661</td>\n",
       "      <td>0.042773</td>\n",
       "      <td>0.049321</td>\n",
       "      <td>0.031833</td>\n",
       "      <td>0.000713</td>\n",
       "      <td>-0.000005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>YLR413W</td>\n",
       "      <td>YBR092C</td>\n",
       "      <td>0.000036</td>\n",
       "      <td>0.006613</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>0.001546</td>\n",
       "      <td>-0.000233</td>\n",
       "      <td>0.000598</td>\n",
       "      <td>-0.003806</td>\n",
       "      <td>0.571926</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003317</td>\n",
       "      <td>0.000083</td>\n",
       "      <td>0.000986</td>\n",
       "      <td>0.000874</td>\n",
       "      <td>0.003003</td>\n",
       "      <td>-0.001001</td>\n",
       "      <td>0.058173</td>\n",
       "      <td>0.021117</td>\n",
       "      <td>-0.000972</td>\n",
       "      <td>0.000523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>YBR093C</td>\n",
       "      <td>YHR215W</td>\n",
       "      <td>-0.000260</td>\n",
       "      <td>0.012187</td>\n",
       "      <td>0.001013</td>\n",
       "      <td>0.001260</td>\n",
       "      <td>-0.000480</td>\n",
       "      <td>0.003908</td>\n",
       "      <td>0.007048</td>\n",
       "      <td>0.218182</td>\n",
       "      <td>...</td>\n",
       "      <td>0.004854</td>\n",
       "      <td>-0.000744</td>\n",
       "      <td>0.001551</td>\n",
       "      <td>0.017854</td>\n",
       "      <td>0.005476</td>\n",
       "      <td>-0.008207</td>\n",
       "      <td>0.063263</td>\n",
       "      <td>0.019714</td>\n",
       "      <td>0.000217</td>\n",
       "      <td>0.000260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>YMR251W.A</td>\n",
       "      <td>YBR093C</td>\n",
       "      <td>-0.000139</td>\n",
       "      <td>0.015429</td>\n",
       "      <td>-0.002134</td>\n",
       "      <td>0.000054</td>\n",
       "      <td>-0.000091</td>\n",
       "      <td>-0.003352</td>\n",
       "      <td>-0.001355</td>\n",
       "      <td>-0.256765</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005976</td>\n",
       "      <td>0.005280</td>\n",
       "      <td>-0.001222</td>\n",
       "      <td>0.005584</td>\n",
       "      <td>-0.005196</td>\n",
       "      <td>-0.001876</td>\n",
       "      <td>-0.070767</td>\n",
       "      <td>0.005922</td>\n",
       "      <td>0.000128</td>\n",
       "      <td>0.000242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>YLR413W</td>\n",
       "      <td>YPR036W.A</td>\n",
       "      <td>0.000139</td>\n",
       "      <td>0.009349</td>\n",
       "      <td>0.000877</td>\n",
       "      <td>-0.003568</td>\n",
       "      <td>-0.000121</td>\n",
       "      <td>-0.005225</td>\n",
       "      <td>0.008626</td>\n",
       "      <td>-0.244071</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003692</td>\n",
       "      <td>-0.000207</td>\n",
       "      <td>-0.001891</td>\n",
       "      <td>0.000239</td>\n",
       "      <td>-0.005818</td>\n",
       "      <td>0.000186</td>\n",
       "      <td>-0.040909</td>\n",
       "      <td>-0.039115</td>\n",
       "      <td>0.000049</td>\n",
       "      <td>-0.001209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>YMR251W.A</td>\n",
       "      <td>YBR092C</td>\n",
       "      <td>0.000054</td>\n",
       "      <td>0.011496</td>\n",
       "      <td>0.000100</td>\n",
       "      <td>0.000073</td>\n",
       "      <td>0.000089</td>\n",
       "      <td>-0.000647</td>\n",
       "      <td>0.001089</td>\n",
       "      <td>-0.527807</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005362</td>\n",
       "      <td>-0.003818</td>\n",
       "      <td>-0.003682</td>\n",
       "      <td>-0.002393</td>\n",
       "      <td>-0.004039</td>\n",
       "      <td>-0.002566</td>\n",
       "      <td>-0.061080</td>\n",
       "      <td>0.006671</td>\n",
       "      <td>-0.001304</td>\n",
       "      <td>-0.000193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>YLR413W</td>\n",
       "      <td>YPR149W</td>\n",
       "      <td>0.000108</td>\n",
       "      <td>-0.009459</td>\n",
       "      <td>0.000800</td>\n",
       "      <td>-0.003258</td>\n",
       "      <td>0.000137</td>\n",
       "      <td>-0.001312</td>\n",
       "      <td>-0.000046</td>\n",
       "      <td>-0.241024</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.004849</td>\n",
       "      <td>0.000054</td>\n",
       "      <td>-0.001993</td>\n",
       "      <td>0.000578</td>\n",
       "      <td>-0.002003</td>\n",
       "      <td>-0.000317</td>\n",
       "      <td>-0.041856</td>\n",
       "      <td>0.001212</td>\n",
       "      <td>-0.003195</td>\n",
       "      <td>0.000261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>YLR413W</td>\n",
       "      <td>YMR251W.A</td>\n",
       "      <td>0.000120</td>\n",
       "      <td>0.011821</td>\n",
       "      <td>0.001089</td>\n",
       "      <td>0.000106</td>\n",
       "      <td>-0.000132</td>\n",
       "      <td>-0.004375</td>\n",
       "      <td>-0.000638</td>\n",
       "      <td>-0.338195</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005092</td>\n",
       "      <td>-0.000335</td>\n",
       "      <td>-0.000794</td>\n",
       "      <td>-0.000793</td>\n",
       "      <td>-0.004532</td>\n",
       "      <td>0.000943</td>\n",
       "      <td>-0.050784</td>\n",
       "      <td>0.009587</td>\n",
       "      <td>0.000592</td>\n",
       "      <td>-0.001010</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 134 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         gene1      gene2  dal81_AmmoniumSulfate  dal81_CStarve  \\\n",
       "0      YBR093C    YAR071W              -0.000012       0.011596   \n",
       "1      YLR413W    YBR093C              -0.000095       0.008875   \n",
       "2      YHR215W    YAR071W               0.000029       0.012201   \n",
       "3      YLR413W    YBR092C               0.000036       0.006613   \n",
       "4      YBR093C    YHR215W              -0.000260       0.012187   \n",
       "..         ...        ...                    ...            ...   \n",
       "995  YMR251W.A    YBR093C              -0.000139       0.015429   \n",
       "996    YLR413W  YPR036W.A               0.000139       0.009349   \n",
       "997  YMR251W.A    YBR092C               0.000054       0.011496   \n",
       "998    YLR413W    YPR149W               0.000108      -0.009459   \n",
       "999    YLR413W  YMR251W.A               0.000120       0.011821   \n",
       "\n",
       "     dal81_Glutamine  dal81_MinimalEtOH  dal81_MinimalGlucose  dal81_Proline  \\\n",
       "0           0.000796           0.001114              0.000197       0.002902   \n",
       "1          -0.000123           0.001146              0.000237       0.003097   \n",
       "2           0.003357           0.002399             -0.000579       0.004781   \n",
       "3           0.000006           0.001546             -0.000233       0.000598   \n",
       "4           0.001013           0.001260             -0.000480       0.003908   \n",
       "..               ...                ...                   ...            ...   \n",
       "995        -0.002134           0.000054             -0.000091      -0.003352   \n",
       "996         0.000877          -0.003568             -0.000121      -0.005225   \n",
       "997         0.000100           0.000073              0.000089      -0.000647   \n",
       "998         0.000800          -0.003258              0.000137      -0.001312   \n",
       "999         0.001089           0.000106             -0.000132      -0.004375   \n",
       "\n",
       "     dal81_Urea  dal81_YPD  ...  gzf3_CStarve  gzf3_Glutamine  \\\n",
       "0      0.003674   0.238742  ...      0.004535       -0.000906   \n",
       "1      0.004737   0.278228  ...      0.003697       -0.000115   \n",
       "2      0.002572   0.246591  ...      0.005073        0.000373   \n",
       "3     -0.003806   0.571926  ...      0.003317        0.000083   \n",
       "4      0.007048   0.218182  ...      0.004854       -0.000744   \n",
       "..          ...        ...  ...           ...             ...   \n",
       "995   -0.001355  -0.256765  ...      0.005976        0.005280   \n",
       "996    0.008626  -0.244071  ...      0.003692       -0.000207   \n",
       "997    0.001089  -0.527807  ...      0.005362       -0.003818   \n",
       "998   -0.000046  -0.241024  ...     -0.004849        0.000054   \n",
       "999   -0.000638  -0.338195  ...      0.005092       -0.000335   \n",
       "\n",
       "     gzf3_MinimalEtOH  gzf3_MinimalGlucose  gzf3_Proline  gzf3_Urea  gzf3_YPD  \\\n",
       "0            0.000253             0.013554      0.005389  -0.007587  0.073221   \n",
       "1            0.000327            -0.002040      0.003864  -0.000732  0.067399   \n",
       "2            0.000778             0.016841      0.006661   0.042773  0.049321   \n",
       "3            0.000986             0.000874      0.003003  -0.001001  0.058173   \n",
       "4            0.001551             0.017854      0.005476  -0.008207  0.063263   \n",
       "..                ...                  ...           ...        ...       ...   \n",
       "995         -0.001222             0.005584     -0.005196  -0.001876 -0.070767   \n",
       "996         -0.001891             0.000239     -0.005818   0.000186 -0.040909   \n",
       "997         -0.003682            -0.002393     -0.004039  -0.002566 -0.061080   \n",
       "998         -0.001993             0.000578     -0.002003  -0.000317 -0.041856   \n",
       "999         -0.000794            -0.000793     -0.004532   0.000943 -0.050784   \n",
       "\n",
       "     gzf3_YPDDiauxic  gzf3_YPDRapa  gzf3_YPEtOH  \n",
       "0           0.018696      0.000068    -0.000003  \n",
       "1           0.018746      0.000096    -0.000656  \n",
       "2           0.031833      0.000713    -0.000005  \n",
       "3           0.021117     -0.000972     0.000523  \n",
       "4           0.019714      0.000217     0.000260  \n",
       "..               ...           ...          ...  \n",
       "995         0.005922      0.000128     0.000242  \n",
       "996        -0.039115      0.000049    -0.001209  \n",
       "997         0.006671     -0.001304    -0.000193  \n",
       "998         0.001212     -0.003195     0.000261  \n",
       "999         0.009587      0.000592    -0.001010  \n",
       "\n",
       "[1000 rows x 134 columns]"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_spcc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "old_spcc['edge'] = old_spcc.gene1 + '_' + old_spcc.gene2\n",
    "old_spcc.set_index('edge', inplace = True)\n",
    "new_spcc['edge'] = new_spcc.gene1 + '_' + new_spcc.gene2\n",
    "new_spcc.set_index('edge', inplace = True)\n",
    "\n",
    "len(old_spcc.index.tolist())\n",
    "len(new_spcc.index.tolist())\n",
    "len(set(old_spcc.index.tolist()).intersection(new_spcc.index.tolist()))\n",
    "\n",
    "common = list(set(old_spcc.index.tolist()).intersection(new_spcc.index.tolist()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gene1</th>\n",
       "      <th>gene2</th>\n",
       "      <th>dal81_AmmoniumSulfate</th>\n",
       "      <th>dal81_CStarve</th>\n",
       "      <th>dal81_Glutamine</th>\n",
       "      <th>dal81_MinimalEtOH</th>\n",
       "      <th>dal81_MinimalGlucose</th>\n",
       "      <th>dal81_Proline</th>\n",
       "      <th>dal81_Urea</th>\n",
       "      <th>dal81_YPD</th>\n",
       "      <th>...</th>\n",
       "      <th>gzf3_CStarve</th>\n",
       "      <th>gzf3_Glutamine</th>\n",
       "      <th>gzf3_MinimalEtOH</th>\n",
       "      <th>gzf3_MinimalGlucose</th>\n",
       "      <th>gzf3_Proline</th>\n",
       "      <th>gzf3_Urea</th>\n",
       "      <th>gzf3_YPD</th>\n",
       "      <th>gzf3_YPDDiauxic</th>\n",
       "      <th>gzf3_YPDRapa</th>\n",
       "      <th>gzf3_YPEtOH</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>edge</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>YDL083C_YNL301C</th>\n",
       "      <td>YDL083C</td>\n",
       "      <td>YNL301C</td>\n",
       "      <td>0.002160</td>\n",
       "      <td>0.029089</td>\n",
       "      <td>0.001935</td>\n",
       "      <td>0.001274</td>\n",
       "      <td>0.011662</td>\n",
       "      <td>0.006098</td>\n",
       "      <td>0.002889</td>\n",
       "      <td>0.134070</td>\n",
       "      <td>...</td>\n",
       "      <td>0.012638</td>\n",
       "      <td>0.003150</td>\n",
       "      <td>0.001280</td>\n",
       "      <td>0.010567</td>\n",
       "      <td>0.005548</td>\n",
       "      <td>0.001984</td>\n",
       "      <td>0.025727</td>\n",
       "      <td>0.144144</td>\n",
       "      <td>0.000592</td>\n",
       "      <td>0.001755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YML073C_YNL096C</th>\n",
       "      <td>YML073C</td>\n",
       "      <td>YNL096C</td>\n",
       "      <td>0.000015</td>\n",
       "      <td>0.027550</td>\n",
       "      <td>0.004777</td>\n",
       "      <td>0.001328</td>\n",
       "      <td>0.016463</td>\n",
       "      <td>0.003870</td>\n",
       "      <td>0.008470</td>\n",
       "      <td>0.134452</td>\n",
       "      <td>...</td>\n",
       "      <td>0.010769</td>\n",
       "      <td>0.006225</td>\n",
       "      <td>0.000877</td>\n",
       "      <td>0.014285</td>\n",
       "      <td>0.002378</td>\n",
       "      <td>0.000203</td>\n",
       "      <td>0.026116</td>\n",
       "      <td>0.124033</td>\n",
       "      <td>-0.000841</td>\n",
       "      <td>0.004160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YBR189W_YKR094C</th>\n",
       "      <td>YBR189W</td>\n",
       "      <td>YKR094C</td>\n",
       "      <td>0.000155</td>\n",
       "      <td>0.027511</td>\n",
       "      <td>0.002709</td>\n",
       "      <td>0.003186</td>\n",
       "      <td>0.015305</td>\n",
       "      <td>0.004582</td>\n",
       "      <td>0.005508</td>\n",
       "      <td>0.139600</td>\n",
       "      <td>...</td>\n",
       "      <td>0.013324</td>\n",
       "      <td>0.004119</td>\n",
       "      <td>0.001485</td>\n",
       "      <td>0.012518</td>\n",
       "      <td>0.004889</td>\n",
       "      <td>-0.000339</td>\n",
       "      <td>0.027592</td>\n",
       "      <td>0.131980</td>\n",
       "      <td>0.000336</td>\n",
       "      <td>0.002884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YOR224C_YBR093C</th>\n",
       "      <td>YOR224C</td>\n",
       "      <td>YBR093C</td>\n",
       "      <td>0.000209</td>\n",
       "      <td>0.018571</td>\n",
       "      <td>-0.000565</td>\n",
       "      <td>0.001060</td>\n",
       "      <td>-0.001729</td>\n",
       "      <td>0.004270</td>\n",
       "      <td>0.011530</td>\n",
       "      <td>0.184360</td>\n",
       "      <td>...</td>\n",
       "      <td>0.007735</td>\n",
       "      <td>0.001310</td>\n",
       "      <td>-0.000474</td>\n",
       "      <td>0.014741</td>\n",
       "      <td>0.005714</td>\n",
       "      <td>0.001251</td>\n",
       "      <td>0.057721</td>\n",
       "      <td>0.022062</td>\n",
       "      <td>-0.000116</td>\n",
       "      <td>0.000717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YDR144C_YMR181C</th>\n",
       "      <td>YDR144C</td>\n",
       "      <td>YMR181C</td>\n",
       "      <td>-0.003450</td>\n",
       "      <td>-0.020480</td>\n",
       "      <td>-0.001319</td>\n",
       "      <td>0.000604</td>\n",
       "      <td>-0.004350</td>\n",
       "      <td>0.005394</td>\n",
       "      <td>-0.000512</td>\n",
       "      <td>-0.142906</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.012442</td>\n",
       "      <td>-0.000045</td>\n",
       "      <td>-0.004159</td>\n",
       "      <td>-0.003308</td>\n",
       "      <td>-0.001170</td>\n",
       "      <td>-0.003558</td>\n",
       "      <td>-0.027176</td>\n",
       "      <td>-0.048104</td>\n",
       "      <td>-0.000206</td>\n",
       "      <td>-0.000955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YLR413W_YNL301C</th>\n",
       "      <td>YLR413W</td>\n",
       "      <td>YNL301C</td>\n",
       "      <td>0.000291</td>\n",
       "      <td>0.013821</td>\n",
       "      <td>0.000281</td>\n",
       "      <td>0.001187</td>\n",
       "      <td>-0.001982</td>\n",
       "      <td>0.004946</td>\n",
       "      <td>0.003605</td>\n",
       "      <td>0.245646</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005925</td>\n",
       "      <td>-0.000133</td>\n",
       "      <td>0.000402</td>\n",
       "      <td>-0.001676</td>\n",
       "      <td>0.004498</td>\n",
       "      <td>0.000622</td>\n",
       "      <td>0.039531</td>\n",
       "      <td>0.061555</td>\n",
       "      <td>-0.000563</td>\n",
       "      <td>-0.001802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YMR181C_YNL096C</th>\n",
       "      <td>YMR181C</td>\n",
       "      <td>YNL096C</td>\n",
       "      <td>-0.001845</td>\n",
       "      <td>-0.022730</td>\n",
       "      <td>0.002431</td>\n",
       "      <td>-0.001951</td>\n",
       "      <td>-0.013741</td>\n",
       "      <td>0.003272</td>\n",
       "      <td>0.005537</td>\n",
       "      <td>-0.130045</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.012073</td>\n",
       "      <td>-0.001308</td>\n",
       "      <td>-0.001280</td>\n",
       "      <td>-0.006069</td>\n",
       "      <td>-0.000541</td>\n",
       "      <td>0.001626</td>\n",
       "      <td>-0.026524</td>\n",
       "      <td>-0.064641</td>\n",
       "      <td>-0.000495</td>\n",
       "      <td>-0.001023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YLR449W_YMR251W.A</th>\n",
       "      <td>YLR449W</td>\n",
       "      <td>YMR251W.A</td>\n",
       "      <td>0.000161</td>\n",
       "      <td>0.015017</td>\n",
       "      <td>0.010372</td>\n",
       "      <td>0.000090</td>\n",
       "      <td>0.000910</td>\n",
       "      <td>-0.004606</td>\n",
       "      <td>-0.001300</td>\n",
       "      <td>-0.244722</td>\n",
       "      <td>...</td>\n",
       "      <td>0.006386</td>\n",
       "      <td>0.002135</td>\n",
       "      <td>0.001521</td>\n",
       "      <td>0.003094</td>\n",
       "      <td>-0.003963</td>\n",
       "      <td>-0.000647</td>\n",
       "      <td>-0.046611</td>\n",
       "      <td>0.014842</td>\n",
       "      <td>-0.001777</td>\n",
       "      <td>0.001153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YDR525W.A_YEL054C</th>\n",
       "      <td>YDR525W.A</td>\n",
       "      <td>YEL054C</td>\n",
       "      <td>0.001126</td>\n",
       "      <td>-0.015597</td>\n",
       "      <td>-0.001116</td>\n",
       "      <td>0.003043</td>\n",
       "      <td>-0.008710</td>\n",
       "      <td>0.003836</td>\n",
       "      <td>0.001128</td>\n",
       "      <td>-0.136097</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.008118</td>\n",
       "      <td>-0.001431</td>\n",
       "      <td>-0.002510</td>\n",
       "      <td>-0.006598</td>\n",
       "      <td>-0.000416</td>\n",
       "      <td>-0.004534</td>\n",
       "      <td>-0.023177</td>\n",
       "      <td>-0.135558</td>\n",
       "      <td>-0.002057</td>\n",
       "      <td>0.001246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YOL083W_YGL076C</th>\n",
       "      <td>YOL083W</td>\n",
       "      <td>YGL076C</td>\n",
       "      <td>-0.003397</td>\n",
       "      <td>-0.036291</td>\n",
       "      <td>-0.003624</td>\n",
       "      <td>-0.005300</td>\n",
       "      <td>-0.006410</td>\n",
       "      <td>0.002056</td>\n",
       "      <td>0.002427</td>\n",
       "      <td>-0.074238</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.017265</td>\n",
       "      <td>-0.003822</td>\n",
       "      <td>-0.004921</td>\n",
       "      <td>-0.009925</td>\n",
       "      <td>0.003140</td>\n",
       "      <td>-0.004173</td>\n",
       "      <td>-0.012359</td>\n",
       "      <td>-0.156406</td>\n",
       "      <td>-0.000110</td>\n",
       "      <td>-0.000232</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>732 rows × 134 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       gene1      gene2  dal81_AmmoniumSulfate  dal81_CStarve  \\\n",
       "edge                                                                            \n",
       "YDL083C_YNL301C      YDL083C    YNL301C               0.002160       0.029089   \n",
       "YML073C_YNL096C      YML073C    YNL096C               0.000015       0.027550   \n",
       "YBR189W_YKR094C      YBR189W    YKR094C               0.000155       0.027511   \n",
       "YOR224C_YBR093C      YOR224C    YBR093C               0.000209       0.018571   \n",
       "YDR144C_YMR181C      YDR144C    YMR181C              -0.003450      -0.020480   \n",
       "...                      ...        ...                    ...            ...   \n",
       "YLR413W_YNL301C      YLR413W    YNL301C               0.000291       0.013821   \n",
       "YMR181C_YNL096C      YMR181C    YNL096C              -0.001845      -0.022730   \n",
       "YLR449W_YMR251W.A    YLR449W  YMR251W.A               0.000161       0.015017   \n",
       "YDR525W.A_YEL054C  YDR525W.A    YEL054C               0.001126      -0.015597   \n",
       "YOL083W_YGL076C      YOL083W    YGL076C              -0.003397      -0.036291   \n",
       "\n",
       "                   dal81_Glutamine  dal81_MinimalEtOH  dal81_MinimalGlucose  \\\n",
       "edge                                                                          \n",
       "YDL083C_YNL301C           0.001935           0.001274              0.011662   \n",
       "YML073C_YNL096C           0.004777           0.001328              0.016463   \n",
       "YBR189W_YKR094C           0.002709           0.003186              0.015305   \n",
       "YOR224C_YBR093C          -0.000565           0.001060             -0.001729   \n",
       "YDR144C_YMR181C          -0.001319           0.000604             -0.004350   \n",
       "...                            ...                ...                   ...   \n",
       "YLR413W_YNL301C           0.000281           0.001187             -0.001982   \n",
       "YMR181C_YNL096C           0.002431          -0.001951             -0.013741   \n",
       "YLR449W_YMR251W.A         0.010372           0.000090              0.000910   \n",
       "YDR525W.A_YEL054C        -0.001116           0.003043             -0.008710   \n",
       "YOL083W_YGL076C          -0.003624          -0.005300             -0.006410   \n",
       "\n",
       "                   dal81_Proline  dal81_Urea  dal81_YPD  ...  gzf3_CStarve  \\\n",
       "edge                                                     ...                 \n",
       "YDL083C_YNL301C         0.006098    0.002889   0.134070  ...      0.012638   \n",
       "YML073C_YNL096C         0.003870    0.008470   0.134452  ...      0.010769   \n",
       "YBR189W_YKR094C         0.004582    0.005508   0.139600  ...      0.013324   \n",
       "YOR224C_YBR093C         0.004270    0.011530   0.184360  ...      0.007735   \n",
       "YDR144C_YMR181C         0.005394   -0.000512  -0.142906  ...     -0.012442   \n",
       "...                          ...         ...        ...  ...           ...   \n",
       "YLR413W_YNL301C         0.004946    0.003605   0.245646  ...      0.005925   \n",
       "YMR181C_YNL096C         0.003272    0.005537  -0.130045  ...     -0.012073   \n",
       "YLR449W_YMR251W.A      -0.004606   -0.001300  -0.244722  ...      0.006386   \n",
       "YDR525W.A_YEL054C       0.003836    0.001128  -0.136097  ...     -0.008118   \n",
       "YOL083W_YGL076C         0.002056    0.002427  -0.074238  ...     -0.017265   \n",
       "\n",
       "                   gzf3_Glutamine  gzf3_MinimalEtOH  gzf3_MinimalGlucose  \\\n",
       "edge                                                                       \n",
       "YDL083C_YNL301C          0.003150          0.001280             0.010567   \n",
       "YML073C_YNL096C          0.006225          0.000877             0.014285   \n",
       "YBR189W_YKR094C          0.004119          0.001485             0.012518   \n",
       "YOR224C_YBR093C          0.001310         -0.000474             0.014741   \n",
       "YDR144C_YMR181C         -0.000045         -0.004159            -0.003308   \n",
       "...                           ...               ...                  ...   \n",
       "YLR413W_YNL301C         -0.000133          0.000402            -0.001676   \n",
       "YMR181C_YNL096C         -0.001308         -0.001280            -0.006069   \n",
       "YLR449W_YMR251W.A        0.002135          0.001521             0.003094   \n",
       "YDR525W.A_YEL054C       -0.001431         -0.002510            -0.006598   \n",
       "YOL083W_YGL076C         -0.003822         -0.004921            -0.009925   \n",
       "\n",
       "                   gzf3_Proline  gzf3_Urea  gzf3_YPD  gzf3_YPDDiauxic  \\\n",
       "edge                                                                    \n",
       "YDL083C_YNL301C        0.005548   0.001984  0.025727         0.144144   \n",
       "YML073C_YNL096C        0.002378   0.000203  0.026116         0.124033   \n",
       "YBR189W_YKR094C        0.004889  -0.000339  0.027592         0.131980   \n",
       "YOR224C_YBR093C        0.005714   0.001251  0.057721         0.022062   \n",
       "YDR144C_YMR181C       -0.001170  -0.003558 -0.027176        -0.048104   \n",
       "...                         ...        ...       ...              ...   \n",
       "YLR413W_YNL301C        0.004498   0.000622  0.039531         0.061555   \n",
       "YMR181C_YNL096C       -0.000541   0.001626 -0.026524        -0.064641   \n",
       "YLR449W_YMR251W.A     -0.003963  -0.000647 -0.046611         0.014842   \n",
       "YDR525W.A_YEL054C     -0.000416  -0.004534 -0.023177        -0.135558   \n",
       "YOL083W_YGL076C        0.003140  -0.004173 -0.012359        -0.156406   \n",
       "\n",
       "                   gzf3_YPDRapa  gzf3_YPEtOH  \n",
       "edge                                          \n",
       "YDL083C_YNL301C        0.000592     0.001755  \n",
       "YML073C_YNL096C       -0.000841     0.004160  \n",
       "YBR189W_YKR094C        0.000336     0.002884  \n",
       "YOR224C_YBR093C       -0.000116     0.000717  \n",
       "YDR144C_YMR181C       -0.000206    -0.000955  \n",
       "...                         ...          ...  \n",
       "YLR413W_YNL301C       -0.000563    -0.001802  \n",
       "YMR181C_YNL096C       -0.000495    -0.001023  \n",
       "YLR449W_YMR251W.A     -0.001777     0.001153  \n",
       "YDR525W.A_YEL054C     -0.002057     0.001246  \n",
       "YOL083W_YGL076C       -0.000110    -0.000232  \n",
       "\n",
       "[732 rows x 134 columns]"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "old_spcc.loc[common,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gene1</th>\n",
       "      <th>gene2</th>\n",
       "      <th>dal81_AmmoniumSulfate</th>\n",
       "      <th>dal81_CStarve</th>\n",
       "      <th>dal81_Glutamine</th>\n",
       "      <th>dal81_MinimalEtOH</th>\n",
       "      <th>dal81_MinimalGlucose</th>\n",
       "      <th>dal81_Proline</th>\n",
       "      <th>dal81_Urea</th>\n",
       "      <th>dal81_YPD</th>\n",
       "      <th>...</th>\n",
       "      <th>gzf3_CStarve</th>\n",
       "      <th>gzf3_Glutamine</th>\n",
       "      <th>gzf3_MinimalEtOH</th>\n",
       "      <th>gzf3_MinimalGlucose</th>\n",
       "      <th>gzf3_Proline</th>\n",
       "      <th>gzf3_Urea</th>\n",
       "      <th>gzf3_YPD</th>\n",
       "      <th>gzf3_YPDDiauxic</th>\n",
       "      <th>gzf3_YPDRapa</th>\n",
       "      <th>gzf3_YPEtOH</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>edge</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>YDL083C_YNL301C</th>\n",
       "      <td>YDL083C</td>\n",
       "      <td>YNL301C</td>\n",
       "      <td>0.002160</td>\n",
       "      <td>0.029089</td>\n",
       "      <td>0.001935</td>\n",
       "      <td>0.001274</td>\n",
       "      <td>0.011662</td>\n",
       "      <td>0.006098</td>\n",
       "      <td>0.002889</td>\n",
       "      <td>0.134070</td>\n",
       "      <td>...</td>\n",
       "      <td>0.012638</td>\n",
       "      <td>0.003150</td>\n",
       "      <td>0.001280</td>\n",
       "      <td>0.010567</td>\n",
       "      <td>0.005548</td>\n",
       "      <td>0.001984</td>\n",
       "      <td>0.025727</td>\n",
       "      <td>0.144144</td>\n",
       "      <td>0.000592</td>\n",
       "      <td>0.001755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YML073C_YNL096C</th>\n",
       "      <td>YML073C</td>\n",
       "      <td>YNL096C</td>\n",
       "      <td>0.000015</td>\n",
       "      <td>0.027550</td>\n",
       "      <td>0.004777</td>\n",
       "      <td>0.001328</td>\n",
       "      <td>0.016463</td>\n",
       "      <td>0.003870</td>\n",
       "      <td>0.008470</td>\n",
       "      <td>0.134452</td>\n",
       "      <td>...</td>\n",
       "      <td>0.010769</td>\n",
       "      <td>0.006225</td>\n",
       "      <td>0.000877</td>\n",
       "      <td>0.014285</td>\n",
       "      <td>0.002378</td>\n",
       "      <td>0.000203</td>\n",
       "      <td>0.026116</td>\n",
       "      <td>0.124033</td>\n",
       "      <td>-0.000841</td>\n",
       "      <td>0.004160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YBR189W_YKR094C</th>\n",
       "      <td>YBR189W</td>\n",
       "      <td>YKR094C</td>\n",
       "      <td>0.000155</td>\n",
       "      <td>0.027511</td>\n",
       "      <td>0.002709</td>\n",
       "      <td>0.003186</td>\n",
       "      <td>0.015305</td>\n",
       "      <td>0.004582</td>\n",
       "      <td>0.005508</td>\n",
       "      <td>0.139600</td>\n",
       "      <td>...</td>\n",
       "      <td>0.013324</td>\n",
       "      <td>0.004119</td>\n",
       "      <td>0.001485</td>\n",
       "      <td>0.012518</td>\n",
       "      <td>0.004889</td>\n",
       "      <td>-0.000339</td>\n",
       "      <td>0.027592</td>\n",
       "      <td>0.131980</td>\n",
       "      <td>0.000336</td>\n",
       "      <td>0.002884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YOR224C_YBR093C</th>\n",
       "      <td>YOR224C</td>\n",
       "      <td>YBR093C</td>\n",
       "      <td>0.000209</td>\n",
       "      <td>0.018571</td>\n",
       "      <td>-0.000565</td>\n",
       "      <td>0.001060</td>\n",
       "      <td>-0.001729</td>\n",
       "      <td>0.004270</td>\n",
       "      <td>0.011530</td>\n",
       "      <td>0.184360</td>\n",
       "      <td>...</td>\n",
       "      <td>0.007735</td>\n",
       "      <td>0.001310</td>\n",
       "      <td>-0.000474</td>\n",
       "      <td>0.014741</td>\n",
       "      <td>0.005714</td>\n",
       "      <td>0.001251</td>\n",
       "      <td>0.057721</td>\n",
       "      <td>0.022062</td>\n",
       "      <td>-0.000116</td>\n",
       "      <td>0.000717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YDR144C_YMR181C</th>\n",
       "      <td>YDR144C</td>\n",
       "      <td>YMR181C</td>\n",
       "      <td>-0.003450</td>\n",
       "      <td>-0.020480</td>\n",
       "      <td>-0.001319</td>\n",
       "      <td>0.000604</td>\n",
       "      <td>-0.004350</td>\n",
       "      <td>0.005394</td>\n",
       "      <td>-0.000512</td>\n",
       "      <td>-0.142906</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.012442</td>\n",
       "      <td>-0.000045</td>\n",
       "      <td>-0.004159</td>\n",
       "      <td>-0.003308</td>\n",
       "      <td>-0.001170</td>\n",
       "      <td>-0.003558</td>\n",
       "      <td>-0.027176</td>\n",
       "      <td>-0.048104</td>\n",
       "      <td>-0.000206</td>\n",
       "      <td>-0.000955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YLR413W_YNL301C</th>\n",
       "      <td>YLR413W</td>\n",
       "      <td>YNL301C</td>\n",
       "      <td>0.000291</td>\n",
       "      <td>0.013821</td>\n",
       "      <td>0.000281</td>\n",
       "      <td>0.001187</td>\n",
       "      <td>-0.001982</td>\n",
       "      <td>0.004946</td>\n",
       "      <td>0.003605</td>\n",
       "      <td>0.245646</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005925</td>\n",
       "      <td>-0.000133</td>\n",
       "      <td>0.000402</td>\n",
       "      <td>-0.001676</td>\n",
       "      <td>0.004498</td>\n",
       "      <td>0.000622</td>\n",
       "      <td>0.039531</td>\n",
       "      <td>0.061555</td>\n",
       "      <td>-0.000563</td>\n",
       "      <td>-0.001802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YMR181C_YNL096C</th>\n",
       "      <td>YMR181C</td>\n",
       "      <td>YNL096C</td>\n",
       "      <td>-0.001845</td>\n",
       "      <td>-0.022730</td>\n",
       "      <td>0.002431</td>\n",
       "      <td>-0.001951</td>\n",
       "      <td>-0.013741</td>\n",
       "      <td>0.003272</td>\n",
       "      <td>0.005537</td>\n",
       "      <td>-0.130045</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.012073</td>\n",
       "      <td>-0.001308</td>\n",
       "      <td>-0.001280</td>\n",
       "      <td>-0.006069</td>\n",
       "      <td>-0.000541</td>\n",
       "      <td>0.001626</td>\n",
       "      <td>-0.026524</td>\n",
       "      <td>-0.064641</td>\n",
       "      <td>-0.000495</td>\n",
       "      <td>-0.001023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YLR449W_YMR251W.A</th>\n",
       "      <td>YLR449W</td>\n",
       "      <td>YMR251W.A</td>\n",
       "      <td>0.000161</td>\n",
       "      <td>0.015017</td>\n",
       "      <td>0.010372</td>\n",
       "      <td>0.000090</td>\n",
       "      <td>0.000910</td>\n",
       "      <td>-0.004606</td>\n",
       "      <td>-0.001300</td>\n",
       "      <td>-0.244722</td>\n",
       "      <td>...</td>\n",
       "      <td>0.006386</td>\n",
       "      <td>0.002135</td>\n",
       "      <td>0.001521</td>\n",
       "      <td>0.003094</td>\n",
       "      <td>-0.003963</td>\n",
       "      <td>-0.000647</td>\n",
       "      <td>-0.046611</td>\n",
       "      <td>0.014842</td>\n",
       "      <td>-0.001777</td>\n",
       "      <td>0.001153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YDR525W.A_YEL054C</th>\n",
       "      <td>YDR525W.A</td>\n",
       "      <td>YEL054C</td>\n",
       "      <td>0.001126</td>\n",
       "      <td>-0.015597</td>\n",
       "      <td>-0.001116</td>\n",
       "      <td>0.003043</td>\n",
       "      <td>-0.008710</td>\n",
       "      <td>0.003836</td>\n",
       "      <td>0.001128</td>\n",
       "      <td>-0.136097</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.008118</td>\n",
       "      <td>-0.001431</td>\n",
       "      <td>-0.002510</td>\n",
       "      <td>-0.006598</td>\n",
       "      <td>-0.000416</td>\n",
       "      <td>-0.004534</td>\n",
       "      <td>-0.023177</td>\n",
       "      <td>-0.135558</td>\n",
       "      <td>-0.002057</td>\n",
       "      <td>0.001246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YOL083W_YGL076C</th>\n",
       "      <td>YOL083W</td>\n",
       "      <td>YGL076C</td>\n",
       "      <td>-0.003397</td>\n",
       "      <td>-0.036291</td>\n",
       "      <td>-0.003624</td>\n",
       "      <td>-0.005300</td>\n",
       "      <td>-0.006410</td>\n",
       "      <td>0.002056</td>\n",
       "      <td>0.002427</td>\n",
       "      <td>-0.074238</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.017265</td>\n",
       "      <td>-0.003822</td>\n",
       "      <td>-0.004921</td>\n",
       "      <td>-0.009925</td>\n",
       "      <td>0.003140</td>\n",
       "      <td>-0.004173</td>\n",
       "      <td>-0.012359</td>\n",
       "      <td>-0.156406</td>\n",
       "      <td>-0.000110</td>\n",
       "      <td>-0.000232</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>732 rows × 134 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       gene1      gene2  dal81_AmmoniumSulfate  dal81_CStarve  \\\n",
       "edge                                                                            \n",
       "YDL083C_YNL301C      YDL083C    YNL301C               0.002160       0.029089   \n",
       "YML073C_YNL096C      YML073C    YNL096C               0.000015       0.027550   \n",
       "YBR189W_YKR094C      YBR189W    YKR094C               0.000155       0.027511   \n",
       "YOR224C_YBR093C      YOR224C    YBR093C               0.000209       0.018571   \n",
       "YDR144C_YMR181C      YDR144C    YMR181C              -0.003450      -0.020480   \n",
       "...                      ...        ...                    ...            ...   \n",
       "YLR413W_YNL301C      YLR413W    YNL301C               0.000291       0.013821   \n",
       "YMR181C_YNL096C      YMR181C    YNL096C              -0.001845      -0.022730   \n",
       "YLR449W_YMR251W.A    YLR449W  YMR251W.A               0.000161       0.015017   \n",
       "YDR525W.A_YEL054C  YDR525W.A    YEL054C               0.001126      -0.015597   \n",
       "YOL083W_YGL076C      YOL083W    YGL076C              -0.003397      -0.036291   \n",
       "\n",
       "                   dal81_Glutamine  dal81_MinimalEtOH  dal81_MinimalGlucose  \\\n",
       "edge                                                                          \n",
       "YDL083C_YNL301C           0.001935           0.001274              0.011662   \n",
       "YML073C_YNL096C           0.004777           0.001328              0.016463   \n",
       "YBR189W_YKR094C           0.002709           0.003186              0.015305   \n",
       "YOR224C_YBR093C          -0.000565           0.001060             -0.001729   \n",
       "YDR144C_YMR181C          -0.001319           0.000604             -0.004350   \n",
       "...                            ...                ...                   ...   \n",
       "YLR413W_YNL301C           0.000281           0.001187             -0.001982   \n",
       "YMR181C_YNL096C           0.002431          -0.001951             -0.013741   \n",
       "YLR449W_YMR251W.A         0.010372           0.000090              0.000910   \n",
       "YDR525W.A_YEL054C        -0.001116           0.003043             -0.008710   \n",
       "YOL083W_YGL076C          -0.003624          -0.005300             -0.006410   \n",
       "\n",
       "                   dal81_Proline  dal81_Urea  dal81_YPD  ...  gzf3_CStarve  \\\n",
       "edge                                                     ...                 \n",
       "YDL083C_YNL301C         0.006098    0.002889   0.134070  ...      0.012638   \n",
       "YML073C_YNL096C         0.003870    0.008470   0.134452  ...      0.010769   \n",
       "YBR189W_YKR094C         0.004582    0.005508   0.139600  ...      0.013324   \n",
       "YOR224C_YBR093C         0.004270    0.011530   0.184360  ...      0.007735   \n",
       "YDR144C_YMR181C         0.005394   -0.000512  -0.142906  ...     -0.012442   \n",
       "...                          ...         ...        ...  ...           ...   \n",
       "YLR413W_YNL301C         0.004946    0.003605   0.245646  ...      0.005925   \n",
       "YMR181C_YNL096C         0.003272    0.005537  -0.130045  ...     -0.012073   \n",
       "YLR449W_YMR251W.A      -0.004606   -0.001300  -0.244722  ...      0.006386   \n",
       "YDR525W.A_YEL054C       0.003836    0.001128  -0.136097  ...     -0.008118   \n",
       "YOL083W_YGL076C         0.002056    0.002427  -0.074238  ...     -0.017265   \n",
       "\n",
       "                   gzf3_Glutamine  gzf3_MinimalEtOH  gzf3_MinimalGlucose  \\\n",
       "edge                                                                       \n",
       "YDL083C_YNL301C          0.003150          0.001280             0.010567   \n",
       "YML073C_YNL096C          0.006225          0.000877             0.014285   \n",
       "YBR189W_YKR094C          0.004119          0.001485             0.012518   \n",
       "YOR224C_YBR093C          0.001310         -0.000474             0.014741   \n",
       "YDR144C_YMR181C         -0.000045         -0.004159            -0.003308   \n",
       "...                           ...               ...                  ...   \n",
       "YLR413W_YNL301C         -0.000133          0.000402            -0.001676   \n",
       "YMR181C_YNL096C         -0.001308         -0.001280            -0.006069   \n",
       "YLR449W_YMR251W.A        0.002135          0.001521             0.003094   \n",
       "YDR525W.A_YEL054C       -0.001431         -0.002510            -0.006598   \n",
       "YOL083W_YGL076C         -0.003822         -0.004921            -0.009925   \n",
       "\n",
       "                   gzf3_Proline  gzf3_Urea  gzf3_YPD  gzf3_YPDDiauxic  \\\n",
       "edge                                                                    \n",
       "YDL083C_YNL301C        0.005548   0.001984  0.025727         0.144144   \n",
       "YML073C_YNL096C        0.002378   0.000203  0.026116         0.124033   \n",
       "YBR189W_YKR094C        0.004889  -0.000339  0.027592         0.131980   \n",
       "YOR224C_YBR093C        0.005714   0.001251  0.057721         0.022062   \n",
       "YDR144C_YMR181C       -0.001170  -0.003558 -0.027176        -0.048104   \n",
       "...                         ...        ...       ...              ...   \n",
       "YLR413W_YNL301C        0.004498   0.000622  0.039531         0.061555   \n",
       "YMR181C_YNL096C       -0.000541   0.001626 -0.026524        -0.064641   \n",
       "YLR449W_YMR251W.A     -0.003963  -0.000647 -0.046611         0.014842   \n",
       "YDR525W.A_YEL054C     -0.000416  -0.004534 -0.023177        -0.135558   \n",
       "YOL083W_YGL076C        0.003140  -0.004173 -0.012359        -0.156406   \n",
       "\n",
       "                   gzf3_YPDRapa  gzf3_YPEtOH  \n",
       "edge                                          \n",
       "YDL083C_YNL301C        0.000592     0.001755  \n",
       "YML073C_YNL096C       -0.000841     0.004160  \n",
       "YBR189W_YKR094C        0.000336     0.002884  \n",
       "YOR224C_YBR093C       -0.000116     0.000717  \n",
       "YDR144C_YMR181C       -0.000206    -0.000955  \n",
       "...                         ...          ...  \n",
       "YLR413W_YNL301C       -0.000563    -0.001802  \n",
       "YMR181C_YNL096C       -0.000495    -0.001023  \n",
       "YLR449W_YMR251W.A     -0.001777     0.001153  \n",
       "YDR525W.A_YEL054C     -0.002057     0.001246  \n",
       "YOL083W_YGL076C       -0.000110    -0.000232  \n",
       "\n",
       "[732 rows x 134 columns]"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_spcc.loc[common,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dal81_AmmoniumSulfate    732\n",
       "dal81_CStarve            732\n",
       "dal81_Glutamine          732\n",
       "dal81_MinimalEtOH        731\n",
       "dal81_MinimalGlucose     732\n",
       "                        ... \n",
       "gzf3_Urea                732\n",
       "gzf3_YPD                 732\n",
       "gzf3_YPDDiauxic          731\n",
       "gzf3_YPDRapa             732\n",
       "gzf3_YPEtOH              731\n",
       "Length: 132, dtype: int64"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(old_spcc.loc[common,:].iloc[:,2:] == new_spcc.loc[common,:].iloc[:,2:]).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "edge\n",
       "YDL083C_YNL301C      0.001274\n",
       "YML073C_YNL096C      0.001328\n",
       "YBR189W_YKR094C      0.003186\n",
       "YOR224C_YBR093C      0.001060\n",
       "YDR144C_YMR181C      0.000604\n",
       "                       ...   \n",
       "YLR413W_YNL301C      0.001187\n",
       "YMR181C_YNL096C     -0.001951\n",
       "YLR449W_YMR251W.A    0.000090\n",
       "YDR525W.A_YEL054C    0.003043\n",
       "YOL083W_YGL076C     -0.005300\n",
       "Name: dal81_MinimalEtOH, Length: 732, dtype: float64"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "old_spcc.loc[common,'dal81_MinimalEtOH']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "edge\n",
       "YDL083C_YNL301C      0.001274\n",
       "YML073C_YNL096C      0.001328\n",
       "YBR189W_YKR094C      0.003186\n",
       "YOR224C_YBR093C      0.001060\n",
       "YDR144C_YMR181C      0.000604\n",
       "                       ...   \n",
       "YLR413W_YNL301C      0.001187\n",
       "YMR181C_YNL096C     -0.001951\n",
       "YLR449W_YMR251W.A    0.000090\n",
       "YDR525W.A_YEL054C    0.003043\n",
       "YOL083W_YGL076C     -0.005300\n",
       "Name: dal81_MinimalEtOH, Length: 732, dtype: float64"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_spcc.loc[common,'dal81_MinimalEtOH']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = old_spcc.loc[common,'dal81_MinimalEtOH'][new_spcc.loc[common,'dal81_MinimalEtOH']!=old_spcc.loc[common,'dal81_MinimalEtOH']].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.00101733e-05])"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "b = new_spcc.loc[common,'dal81_MinimalEtOH'][new_spcc.loc[common,'dal81_MinimalEtOH']!=old_spcc.loc[common,'dal81_MinimalEtOH']].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.00101733e-05])"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='None', ylabel='None'>"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x = old_spcc.iloc[:, 2:].sum(axis= 1)[:500],y = new_spcc.iloc[:, 2:].sum(axis= 1)[:500])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='None', ylabel='None'>"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkcAAAGxCAYAAABoYBJuAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8g+/7EAAAACXBIWXMAAA9hAAAPYQGoP6dpAAA+HklEQVR4nO3de3RU9b3//1cyAXK/X0hCgHAJF0lMIBYpCC22qK1chLbaY2HxBUQ4rcqx2op6ju1p0UI9ivpF5NeIX0z11FrUCK2rtqBVabkoUIJcgklQSEJCEpJMyH1mfn9Q4g5MCEkmM5PZz8darMXs/Zk97/dMgBef/dl7/BwOh0MAAACQJPl7ugAAAABvQjgCAAAwIBwBAAAYEI4AAAAMCEcAAAAGhCMAAAADwhEAAIBBgKcL8FZtbW2qra3VoEGD5O9PhgQAoD+w2+1qbm5WRESEAgJ6FnMIR52ora3VyZMnPV0GAADogeHDhysmJqZHzyUcdWLQoEGSLry5QUFBHq6mb9hsNhUUFCgtLU0Wi8XT5biFGXuW6NtMfZuxZ8mcfZuxZ6nrvhsbG3Xy5Mn2f8d7gnDUiYun0oKCghQcHOzhavqGzWaTJAUHB5vmD5YZe5bo20x9m7FnyZx9m7Fn6er77s2SGK8PR7t379aGDRt05MgRBQYGateuXVccv3DhQh08eLDDecYDBw70dZkAAMBHeP1K4+DgYC1YsEAPPfTQVT/n4Ycf1oEDB9p/AQAAXC2vnznKyMhQRkaG9uzZ45HXt9ls7VN4vuZiX77anzNm7FmibzP1bcaeJXP2bcaepa77dsX74edwOBy9Poob7NmzR/fff/9VnVY7ceKEHA6HUlJS9O///u+aOXNmt1+voaFBR48e7Wm5AADAg8aNG9fjNcMenTmy2WzqLJv5+fn1aIHZAw88oJEjR2rgwIF6//33df/99+vll19WRkZGj2pMS0vz6QXZ+fn5Sk9PN81iPjP2LNG3mfo2Y8+SOfs2Y89S1303NDSooKCgV6/h0XC0ePFi7d271+m+2NjYLmeJnLn22mvbfz9r1izt2LFD7777bo/DkcVi8fkfOjP0eCkz9izRt5mYsWfJnH2bsWep875d8V54NBzl5ub2+Wv4+/t3OjsFAABwKa+/Wu3ibcBbW1slSc3NzWppaXE6tq6uTn/729/U1NQkm82mHTt26J133unRmiMAAGBOXn+12r59+7Ro0aL2xxkZGUpOTtbOnTslScuWLVN2drZWrFihtrY2Pffcc1q1apX8/f01bNgwrV27VpMmTfJU+QAAoJ/x+nA0efJkHT9+vNP9OTk57b+Pjo7WH/7wB3eUBQAAfJTXn1YDAABwJ6+fOQIAAL6jqKRGpyvqdbqiXkPiQzUkPlQjkiM9XVYHhCMAANDnqmobVVPfrP/98zF9fLRCNvuFK8knjIjWXfPSvSogcVoNAAD0qZIKq8qrGlRSUa/E2FAtvnW85k4fIUk6XFStkrP1Hq6wI2aOAABAnyksqZH1fIt+/9cTyi+sbN8+PjVac6ePUN4HRTpd4V3hiJkjAADQJwpLarT57cM6WVbXIRhJ0pHiasVGBsni76fkuFAPVegcM0cAAMClqmobVd/QorKz9fr8jLXT9UTVdU3KHhuvpLgQ9xbYBcIRAABwmeLSWlXWNKq4tFaxkUH6+sQhiokIdDp21JBI3ZCZrKgw5/s9hdNqAADAJQpLanTs83M69FmlBg6wqKikVgWnahQROkjjU6M7jM0YFavE2BBFhwcqJiLIQxU7x8wRAADolaraRtVYm7R52xEd+qzjouvRKZGqrW9W2tBITUlPVN35Fo1KiVRSTIiGJ0V4sOrOEY4AAECPnSq3qqW1TZ+V1HYIRtKFRddT0hPV3GJTVFigYiMClT4iVpHhg7xutsiI02oAAKDbqmob9dnpczpZWquPj1bI4u/ffu8io+q6JoWFDFBibIgGx4Z4fTCSmDkCAADdVFxaq+YWm7b86YgOF1a1bzfeu+iixJgQtbS2KSE62Kvugn0lhCMAAHDVCktqdKSoShGhgzoEI+nL02gWfz/Z7A5ljIpValKEBg20KNVL1xc5QzgCAABdqqptVFNzmxoaWnWg4GynN25sarbpe98YreiIII0eEqHIMO+7Gq0rhCMAAHBFhSU1KqmoV8nZeiXHhWr/sQpljIp1OjYhOlhBgywa7MVXo3WFcAQAADpVWFKjnLfydbioWhGhA/X1SSmy2R2qrGnU+NRoHSmubh87YWSMUpMiNDwp3IMV9x7hCAAAXKaqtlF19c0qrajX4aILAai+obX9btd5HxRp7vQRmpKeqOq6Jo1OiVRSXGi/D0YSl/IDAIBLFJbUqOCLc9p1qEytNnv7JfrGGSPpQkD6f9uPqOzseSXFhWpkP7karSvMHAEAgHaFJTXa/Panl93p+uIl+hdnjG6dmnphDVJ8qIbEh/Wrq9G6QjgCAAAXvgKkvlkFn9d0eqfri5foF56u0dcnDVFaSqQSYp1ftdafEY4AADC5wpIa+Us6WVqn0sp6p2OaW2xaeMs4xUcHKSEqRAEWi08GI4k1RwAAmFpxaY2qa5pUVnlen5+pa19wfangwAB9caZWSbEhCgkK0LDE/r/wujPMHAEAYEKfl9Wp1WbTiS9qVVZ1XsMSwzQ0IVwny2ovu0Q/Y1Sshg4O0/gRMT6z6PpKCEcAAJjMybJatdns2vz2EeUXfrm+aMns8SoqqdXolMj2S/QTY0I1ZlikLP7+Pj1bZEQ4AgDAJKpqG3WmqkE1dY2yOdQhGEnS5m1H9Oj/+YrKqs7rnLVJwxLCNTw5vN98YayrEI4AADCBz8vqVFnTqA8OntbJkjp9ZcJgp+OKS+sUNMiiccNiFBcdZIrTaJciHAEA4OOKS2tVca5RzS1tio8K1sS0eFkszq/JigwbpNTEcA0Y4G+6GaOLCEcAAPiwwpIaNbfY9Ob7J/Rp0ZeLrJfOvkbpI2M7nFpLHxmr0UMjTTlbZEQ4AgDAR50sq1VNTZPOt7R1CEaS9OK2T7VmxVc1a/JQlVWdV3JcqJLjfecrQHqDcAQAgI8pqbDqi3KrPi+rU3x0sAYNtDgdd7ioSpXV5zVrynANGGAhGP0L4QgAAB9yusKq/++tfB04frZ9W/rI2PbvRjNKSQjVlPTBSk2KdHOV3o1wBACADygurdXpCqtKztZr4ph4DU0Iaw9D+YWVuiEzqf270SRpwohoJcWFEoycIBwBANDPFZbUaPPbh/X5GavqG1plszs0PjW6w2xRtbVZP75zkkoq6pUcH6KkONYXdYZwBABAP1ZW1aD6hhZdN36wRiRHKiYiUJU1jcr7oEhT0hPbZ4tCAgPU2NSqqdcmKSUhzNNlezXCEQAA/dDnZ6wKCE/ROWuzXvtLgfILq9r3XZw1qq5rUkjQAA2JD5Uc0siUSILRVXB+Bygvsnv3bi1cuFCTJk3S1KlTr+o5r7zyir75zW8qMzNTs2bN0v79+/u4SgAA3Ke4tEZHT1brd38pUNHpmg7BSJKOFFcrNjJIqUkRumvOBN1501hdmxbHabSr5PUzR8HBwVqwYIHmzJmj9evXdzn+jTfe0P/+7//queee05gxY1RWViaLxfkljAAA9CdVtY06W9Oos9UNGjQwQKfKrRo1JNLp2LrzLRqeGKbI8EANHxzh3kL7Oa8PRxkZGcrIyNCePXu6HGu32/Xss89qzZo1Gjt2rCQpKSmpV69vs9lks9l6dQxvdbEvX+3PGTP2LNG3mfo2Y8+SOfouq2pQ2dl6ffjPUh3+rFJTM5NV39CqmIhAp+NHDYlUWMggpcSF+tT70tVn7YpevT4cdUd5ebnKysp04sQJPfLII5Kkb37zm3rggQc0aNCgHh2zoKDAlSV6pfz8fE+X4HZm7FmibzMxY8+S7/YdmTBCrTaHGltsSogO1sRvj1d4yCC99bdCVdY0anxqtI4Uf3kH7AkjY5QQHaSaM4U6eMaDhfehvvysPRqObDabHA6H031+fn7dPh1WVlYmSfrwww/11ltvqbm5WStXrtSmTZt077339qjGtLQ0BQcH9+i53s5msyk/P1/p6emmOfVoxp4l+jZT32bsWfLtvk9V1Ku11a4t2z/VYcNXgKSPjNHS2dfoxW2fau70EZqSnqi68y0amRyh5PgwpcSHSImZniu8j3T1WTc0NPR6YsOj4Wjx4sXau3ev032xsbHatWtXt44XFBQkSVq2bJkiIyPbX2PLli09DkcWi8Xn/qBdygw9XsqMPUv0bSZm7Fnyrb6LS2tV39CiqtpG+fv7dwhGkpRfWKVvfTVVy+Zeo6aLM0pj4jV0cLiHKnavzj5rV3z+Hg1Hubm5Lj3e8OHDNXDgQJceEwAAdyssqdHhzyr19/wyWRtaNDXD+frZL8qtqqg6r1u+mqrAQQGmCUZ9zevXHNntdrW2tqq1tVWS1NzcLD8/P6chKCgoSLfeeqtefPFFTZgwQS0tLXr55Zc1c+ZMd5cNAEC3VdU2qsbarNKz9XLowiX5AwP8lRwX6nR8SkKorhufoOjwQMVEBLm3WB/m9fc52rdvnzIyMrR06VJVVlYqIyNDN998c/v+ZcuW6YUXXmh//MgjjygyMlIzZszQ3LlzlZ2drbvuussTpQMAcNVKKqyqsTbpTHWDKmoaVFXbJElqabPLIemaEdEdxk8YEa2k2BCNSAonGLmY188cTZ48WcePH+90f05OTofHoaGhevLJJ/u6LAAAXKakwqra+hblvnNER0+e0/+ZPb7DJfpPvbpf9//bRN18/XCVVZ3XkPhQDYkPU3XZZz656NrTvH7mCAAAX1Z2tl419U06W9Oow0XVstkdOnuuUQ7HhS+PveipV/dr5yen9dVrk3RD5hANTXB+qg295/UzRwAA+KKq2kYVfHFOn5fVaXhSuErO1rfvy/ugSHOnj9D8r43STde3qrKmUclxoRocG6JhCSy67muEIwAA3Kyssl4v/+mo/pFfJpvdoYEB/rrn9qwOY/I+KNL2j4r1k4XZmjQmXoGDApQcz5fGugPhCAAANympsKqytlFVtU0aOjhMk8Ym6GRZrfI+KJLD4dCEEdEd7mc0bniUEmKC+cJYNyMcAQDgBiUVVu07Uq5/HC7r8FUf6SNjNHf6CD316n794u4puuV8i05X1Cs5LlRDEkIJRh7AgmwAAPrY6QqrTpbVSX7qEIykC3e6jo0MksXfTyfL6mS32zVmaJRGDYkgGHkIM0cAAPSRkn+FoopzDZKk6rpmp+POWZs1cUy85JCGJ0ZqeBKLrj2JcAQAQB8oqbDq+a2HdOizSln8/S67d5HRyOQITRoTr4jQQRqWSDDyNE6rAQDgYhdnjA59VilJnd67SJKuHRWroQnhyhgdRzDyEswcAQDgQiVnrfrDzhNKiAnusP3ivYtu/+YYnatr0tlzjUpJuHDvIk6jeRdmjgAAcIHPy+p06ESFCk/XKjxkkBJjQzV3+ogOY/I+KNKp8jrlvnNUJ0trNCQ+jEXXXoiZIwAAeumLM3U6cLzissv0rxkRrbnTRyjvgyJJUvrIWMVFBunu29I1NCGMmzp6KcIRAAA9VFXbqPKqBjU0tcrPyWX6nxZV6+brh2v+10cqKixQ8pNSEsKVkkAo8maEIwAAeqCqtlGVNRd+na1tUFubw+m4M1UNigoLVFxksIYnMlvUHxCOAADopuLSWp0916Bjn59TeMhA1VibFRMR5HRscnyIRg2JVGJsqJurRE+xIBsAgG4oKqnRsZPVyi+sUnjIQFXWNKrgixpFhA667DL9zNFxGp8aQzDqZ5g5AgDgKlTVNqqs8rz+993j7fcvkqTxqdEanRKp2vpmpaVE6qbrh6mypklDE8I0emhkpzNK8F7MHAEA0IWq2kY9+9pBFZXWdghG0oVF2LGRQTpnbdLOT04rwN9f3/hKiq5PTyQY9VOEIwAAunCytE6FJTU6e67R6f7quiYlRAdr+OBQDUsMV3Q4oag/47QaAABOVNU2qri0TqfKrYoMG6SvTxrS6XejJcaEKDk+VP/+nUyuRvMBhCMAAC5RVduo9b87oIMFZ9u3jU+N1s1TIjU+NbrD/YwyRsVq7LBopSZHeKJU9AHCEQAABlW1jTpaXN0hGEkX1hZNSU9UWkqkpqQnqu58i4YODldqYjhfGOtjCEcAAPzL52V1euXPx5QQHex0f0urXbGRwYoMHahxw6IVGxXEomsfRDgCAEBSSYVVR4qrtPfTM1p863inY0YkRyh7XIKbK4O7EY4AAKZlXHSdGBOs6rom2ewOVdY0Xra2KDMtTqlJnD4zA8IRAMCUSiqsyn3nmI6erFJtfYskaflt6ZKkvA+KNHf6CE1JT1R1XZPSUqI0LjWaU2gmQTgCAJhOyVmrisvqNCwxTNnjExQWNED5hZUqPVuv9JGxyi+sVN4HRbL4++m68QmaO30kwchECEcAANO4+BUgr/75mPILq9q3j0+N1vUTBquqtkmzrh+q6ycMVmVtoxJjQjQ+NYZgZDLcIRsAYApVtY16Me+wCktqOgQj6cJl+n5+foqLClKAv5+aWto0fniMMkbFcpm+CTFzBADweSUVVhWV1io2KlghQQM1d/oI5X1Q1GFMdV2TQoIGaPyIGE3LHOKhSuENCEcAAJ9WVFKjF9/+tMMXxo5Pjb4sIEWHB2pwTAjfiwZOqwEAfNfnZXU6/vm5DsFIunAaLTYySBZ/P0kXwpKfn59Gp0R6oEp4G2aOAAA+p6q2USdL69TSZlNp5XmnY5qabfr+TWOU+K/ZosTYYBZeQxLhCADgY6rrGvXi24dVbW3StaNiFRMR6HRcUlyIEqKCFRcdxKk0dEA4AgD4hKCwGB3/vEZnqhuUkhCmSeMSNGiARcdOVl92t+uMUbEaOSRCyXFhHqwY3opwBADo98qrG3XiTJv+fuhIhxC0bM41Kiqp1eiUyPa7XSfGhGjMsCiCETrl9eFo9+7d2rBhg44cOaLAwEDt2rWr07GlpaX69re/3WFbY2OjfvCDH+jRRx/t61IBAG5WVduoz07VqLi0VoNjQjQ6JbJDOMp5+1P9csUU1da3qLSyXuOHRysxNpR7F+GKvD4cBQcHa8GCBZozZ47Wr19/xbFJSUk6cOBA++Pa2lpNmzZNN998cx9XCQBwt6raRq3/3QEdLDjbvs3ZJfr7j59VdW2Tls4dr8hQ1haha14fjjIyMpSRkaE9e/Z0+7nbt29XUlKSsrOze/z6NptNNputx8/3Zhf78tX+nDFjzxJ9m6lvs/R8ztqsYyfPdQhG0oVL9KekJ8ri7yeb3SFJGj0kUmOnRSksaKBPvS9m+awv1VXfrng/vD4c9cbWrVs1f/78Xh2joKDARdV4r/z8fE+X4HZm7FmibzPx5Z6DwmL01t/LlRAd4nT/xTtd151v0bWjYhUd3KrPC4/qczfX6S6+/FlfSV/27dFwZLPZ5HA4nO7z8/OTxWLp8bGPHz+uY8eOaePGjT0+hiSlpaUpODi4V8fwVjabTfn5+UpPT+/Ve92fmLFnib7N1LcZej5w/Kz2flquxbeOd7p/1JBIhQQN0LDB4Ro1JEJRYYMkpbi3SDcww2ftTFd9NzQ09Hpiw6PhaPHixdq7d6/TfbGxsVdcfN2VrVu3atq0aUpISOjxMSTJYrH4/A+dGXq8lBl7lujbTHy551MV9bLZHaqsabzsEv30kbGKiwrShJExprl3kS9/1lfSWd+ueC88Go5yc3P75Litra3atm2bHnvssT45PgDAc1ISLlyCn/dBkeZOH9F+if7oIZEalxqt2EjfnO2H+3j9miO73a7W1la1trZKkpqbm+Xn56eBAwd2+pz33ntPDodDM2fOdFeZAIA+UFJhVXFpnb6osGpofJhSk8KVmhSuzLQ4HSw4q7wPimTx99PkCYN1/ZjQf51CA3rH68PRvn37tGjRovbHGRkZSk5O1s6dOyVJy5YtU3Z2tlasWNE+5o033tDs2bOvGKAAAN6tvPK8XnjzkA4WfPmlsRNGROtH383UqjuyVFxap9MVVg2JD9PwxDB9XnhUvri2CO7n9eFo8uTJOn78eKf7c3JyLtv2wgsv9GVJAIA+VHa2XkUltfq83Kobs4dqZvZQPfXqfknS4aJqFZfWaVpmsmIigpQ97sK6UpvN5rNXo8H9/D1dAAAA0oWbOu4+XKZ3936hs7WNamhq1f+8ul9/3n1S9//bxPZxpyvqPVglzMDrZ44AAL6vpMKq57ce0qHPvjyFZrzb9c3XD9fAAH+1tNk1JD7Ug5XCDJg5AgB4VHVdow4XVXUIRtKFu13HRgbJ4u+n0srziosK1oQR0UpN4nvR0LeYOQIAeERJhVUny6w6e65BTa1tTsdcvNv1kPhQ/eDmsUpNCldyfJibK4XZEI4AAG5XUmHVhj8cUn5hpSz+fp3e7To6PFDDB4dqZHIEoQhuQzgCALhdcWmd8gsvnEbr/G7XMRqRFKHrxiUQjOBWhCMAgNudrrB2eHzxbtezJg9TxbkGxYQH6pqRMUqOIxTB/QhHAIA+137vogqrhsWHaejgyxdV531QpGVzr1FqYoRGD41UTIQ5vhsN3oer1QAAfeayexc1tupXuR+rovq8rh0V22Hs+NRoSX4EI3gcM0cAgD5RVduo9b87oIMFZ9u3Xbx30YvbjmjNiq/qW1NTdarcqvioYMVFBSsxNphgBI8jHAEA+kRxaV2HYCRduHfRlPREWfz9dKS4WnfMGuOh6oDOcVoNANAnTpVbnW433rsI8EbMHAEAXKKkwqri0jp98a9F14mxIU7HXbx3EXe6hrciHAEAeq2kwqr/+/pBHS768j5FS2ePV+boOB088eWpNe5dhP6AcAQA6LXi0roOwUhS+6LruTNG6nSFVUPiw5SaFM6Ca3g9whEAoNe+qHC+vujiouvscQlurgjoOcIRAKDbqmobVVxap1PlVqUkhGlCaozTcSy6Rn9EOAIAdEtJhVXPbz2kQ59Vtm/LHB2npbPH68VtR9q3TRgRzaJr9EuEIwDAVauqbVR+YVWHYCRJB0+c1benpeqhhdk6VVGvIfEXrkZj0TX6I8IRAOCqnSq3quRsvdN9Z6rOa96MUW6uCHA9bgIJALhqJ8vqFBMR6HTfEGaJ4CMIRwCAqzYkPkyVNY3/+pLYL2WMimV9EXwGp9UAAFctNSlceR8UanRKpKakJ6q6rklJsaFKHxnD/YvgMwhHAICrFhMRpFV3ZKm4tE4lZ62aOCZeKQlhBCP4FMIRAKBbYiKCFBMRxI0d4bNYcwQAAGBAOAIAADAgHAEAABgQjgAAAAxYkA0AJnLpF8amJoVzpRlwCcIRAJhEVW2j1v/ugA4WnG3flpkWp1V3ZBGQAANOqwGASRSX1nUIRpJ0sOCsikvrPFQR4J0IRwDg46pqG3WwoEIFp8453X+6wurmigDvRjgCAB928VTaz36zW8GDnK+k4AtjgY4IRwDgwy6eSrPZHU6/MDYzLY4vjAUuwYJsAPBhp8q/PGWW90GR5k4foSnpiWpqtmlUSiRXqwFOeP3M0e7du7Vw4UJNmjRJU6dO7XL80aNHdeeddyo7O1vTpk3T2rVrZbfb3VApAHiflISOp8zyPijS/9t+RGOHRyl7XALBCHDC68NRcHCwFixYoIceeuiqxq9atUpZWVnavXu3Xn/9de3cuVOvvfZaH1cJAN4pNSlcmWlxHbalj4q9LDQB+JLXn1bLyMhQRkaG9uzZ0+VYh8OhkpISzZs3TwEBAUpMTNS0adN04sSJHr++zWaTzWbr8fO92cW+fLU/Z8zYs0TfZur70p4jQwfqvtszdbKsTqfK65WSEKrhieGKDB3oU+8Ln7V5dNW3K94PP4fD4ej1Udxgz549uv/++7Vr164rjlu3bp0cDof+4z/+QxUVFVq6dKlWr16tr33ta916vYaGBh09erQXFQOAdwkICFBbW5unywDcYty4cQoODu7Rcz06c2Sz2dRZNvPz85PFYun2MW+88UatXr1aW7Zskc1m0+23364ZM2b0uMa0tLQev7nezmazKT8/X+np6T16r/sjM/Ys0beZ+jZjz5I5+zZjz1LXfTc0NKigoKBXr+HRcLR48WLt3bvX6b7Y2NguZ4kuVVNTo2XLlmn16tW67bbbVFdXp4ceekhPPvmkHnzwwR7VaLFYfP6Hzgw9XsqMPUv0bSZm7FkyZ99m7FnqvG9XvBceDUe5ubkuPd6pU6ckSd/73vckSTExMZo/f742bNjQ43AEAADMxeuvVrPb7WpublZra6skqbm5WS0tLU7HpqamymKxaOvWrbLZbKqpqdFbb72lMWPGuLNkAADQj3l9ONq3b58yMjK0dOlSVVZWKiMjQzfffHP7/mXLlumFF16QJIWGhmrDhg167bXX9JWvfEW33HKLQkJC9Mgjj3iqfAAA0M94/aX8kydP1vHjxzvdn5OTc9n43//+931dFgD0qaraRp0srdOZ6gYNjg7WcO5kDbiN14cjADCbqtpGffTPUjkcDlXVNqm1zabTZ+s17dokAhLgBoQjAPAy5VUN+vuhUh0prm7fNj41WqOHRBKOADfw+jVHAGA25ecaOgQjSTpSXK2Kcw0eqggwF8IRAHiZs+canW6v6GQ7ANciHAGAlxk22PmXwg5LDHdzJYA5EY4AwMuMSolU5ui4DtsyR8dp1JAID1UEmAsLsgHAy8REBGnV97NUXFqn0xVWDYkPUyqX8gNuQzgCAC8UExGkmIggZY9L8HQpgOlwWg0AAMCAcAQAAGBAOAIAADAgHAEAABgQjgAAAAwIRwAAAAaEIwAAAAPCEQAAgAHhCAAAwIBwBAAAYEA4AgAAMCAcAQAAGBCOAAAADHoUjux2u3JycjRr1ixNmDBBp06dkiRt3LhReXl5Li0QAADAnXoUjjZs2KDXX39d9957rywWS/v24cOH65VXXnFZcQAAAO7Wo3D01ltv6Ze//KVuvfVW+ft/eYixY8eqqKjIZcUBAAC4W4/CUWVlpQYPHnzZ9ubmZtnt9l4XBQAA4Ck9CkcZGRn6y1/+ctn2V155RZMmTep1UQAAAJ4S0JMnPfTQQ1qyZIn++c9/qrW1VRs2bNBnn32mU6dO6be//a2rawQAAHCbHs0cXXPNNfrzn/+stLQ03XjjjaqqqtKUKVOUl5en0aNHu7pGAAAAt+nRzJEkRUZG6oc//KErawEAAPC4Hoej6upq5efnq6qq6rJF2N/5znd6XRgAAIAn9Cgc/elPf9Lq1avl7++vqKioDvv8/PwIRwAAoN/qUTh68sknddddd2nlypUdbgIJAADQ3/VoQXZNTY3mzp1LMAIAAD6nR+Ho1ltv1XvvvefqWgAAADyuR6fVwsLC9Nxzz2nXrl1KS0tTQEDHw9x3330uKQ4AAMDdehSODh06pLFjx6qhoUEHDx7ssM/Pz88VdbXbvXu3NmzYoCNHjigwMFC7du3qsrbHH39cx48fV3x8vH76059q5syZLq0JAAD4rh6Fo9zcXFfX0ang4GAtWLBAc+bM0fr16684tq6uTnfffbd++tOfavbs2dq1a5fuuecevf322xo2bJh7CgYAAP1aj9YcXVRfX6+jR4/q6NGjqq+vd1VNHWRkZGjevHkaOnRol2P379+v8PBwzZs3TxaLRdOnT1dGRoby8vL6pDYA3qWqtlEfHy3Xm+9/po+PluuctdnTJQHoh3o0c9TY2KgnnnhCb7zxhtra2i4cKCBACxYs0OrVqxUYGOjSIq+W3W6Xw+G4bPvx48d7fEybzSabzdabsrzWxb58tT9nzNizZI6+z1mb9cxrB3Ww4Gz7tsy0OH3/60k+3felzPBZO2PGvs3Ys9R13654P3oUjn75y1/q448/1saNG5WVlSXpwqzNmjVrtGbNGv3iF7+4quPYbDanYUa6sHapu7cKyMrKUnV1tbZu3ao5c+boo48+0v79+3X99dd36zhGBQUFPX5uf5Gfn+/pEtzOjD1Lvt13o39Mh2AkSQcLzuqmryT7dN+dMWPPkjn7NmPPUt/23aNw9Je//EUvvPCCJk6c2L5t+vTpCgkJ0cqVK686HC1evFh79+51ui82NrbLxdeXioqK0saNG7Vu3TqtXbtWmZmZuuWWWzRw4MBuHccoLS1NwcHBPX6+N7PZbMrPz1d6erpp7lllxp4lc/T99odFTreXVjZo3gzf7ftSZvisnTFj32bsWeq674aGhl5PbPQoHLW2tiooKOiy7YGBge2n2a5GXyzsvu666/T666+3P7799tu1YMGCHh/PYrH4/A+dGXq8lBl7lny775SEcKfbk2KDfbrvzpixZ8mcfZuxZ6nzvl3xXvRoQfYNN9ygn/3sZyoq+vJ/aoWFhfrFL36hadOm9booI7vdrubmZrW2tkqSmpub1dLS0un4I0eOqKWlRefPn9cLL7yg6upqzZs3z6U1AfA+qUnhykyL67AtMy1O0SHOT90DQGd6NHP02GOP6cEHH9S3vvUthYaGSrpw5drUqVP12GOPubTAffv2adGiRe2PMzIylJycrJ07d0qSli1bpuzsbK1YsUKS9NJLL2nnzp2y2+2aPHmyNm/e3KvTagD6h5iIIK26I0vFpXU6XWHVkPgwDU8M0+eFRyWleLo8AP1Ij8JRTEyMNm/erKKiIhUXF8vhcGjEiBEaMWKEq+vT5MmTr3i1WU5OTofHv/71r11eA4D+ISYiSDERQcoelyDpwtqEzz1cE4D+p1vhaPXq1Z3u27Fjh6QLV5k9/vjjvasKAADAQ7oVjq602LqlpUXvv/++WlpaCEcAXKaqtlHFpXU6VW5VSkKYUpPCFRNx+QUhAOAq3QpHzk5Z2e12bd26Vc8//7yioqK0cuVKlxUHwNyqahu1/ncHLrux46o7sghIAPpMj9YcXfTHP/5Rzz77rOrr63XXXXfp3/7t31j8DMBlikvrnN7Ysbi0jnAEoM/0KBzt2LFD69evV3l5uZYsWaJFixb57I0SAXjOqXKr0+2nK6zti64BwNW6FY7+/ve/a/369frss8+0aNEiLV26VGFhYX1VGwATqapt1MnSOp2pbtDg6GANTwpXSoLzv1+GxPP3DoC+061wtGTJEgUGBmrOnDny8/PT5s2bnY677777XFIcAHOoqm3UR/8slcPhUFVtk1rbbDp9tl7ZY+OVmRZ32Zqj1CTnd8MGAFfoVji67rrrJEnFxcUqLi52OsbPz6/3VQEwlfKqBv39UKmOFFe3bxufGq3RQyIvu7EjV6sB6GvdCkd98V1oAFB+rqFDMJKkI8XVqjjXoPEjYjrc2BEA+lqPvlsNAFzp7LlGp9srOtkOAH2JcATA44YNdr7Aelgia4sAuB/hCIDHjUqJVObouA7bMkfHadSQCA9VBMDMenUTSABwhZiIIK36PguvAXgHwhEArxATEcTCawBegdNqAAAABoQjAAAAA8IRAACAAeEIAADAgHAEAABgQDgCAAAwIBwBAAAYEI4AAAAMCEcAAAAGhCMAAAADwhEAAIAB4QgAAMCAcAQAAGBAOAIAADAgHAEAABgQjgAAAAwIRwAAAAaEIwAAAAPCEQAAgAHhCAAAwIBwBAAAYEA4AgAAMPD6cJSTk6PZs2dr4sSJmjFjhp5++mnZbLZOx5eVlWnJkiXKzMzUN77xDb377rturBYAAPR3AZ4uoCt2u12PP/64xo4dq4qKCq1cuVIhISFavny50/H333+/xo8fr+eff16ffPKJfvSjH2n06NFKTU11c+UAAKA/8vqZo+XLlys9PV0DBgxQcnKyZs+erf379zsde/LkSeXn5+u+++5TYGCgpk6dqmnTpikvL8/NVQMAgP7K62eOLrVv3z6NGTPG6b4TJ04oKSlJ4eHh7dvGjh2rI0eO9Pj1bDbbFU/j9WcX+/LV/pwxY88SfZupbzP2LJmzbzP2LHXdtyveD4+GI5vNJofD4XSfn5+fLBZLh225ubkqKCjQ2rVrnT7n/PnzCg0N7bAtPDxc58+f73GNBQUFPX5uf5Gfn+/pEtzOjD1L9G0mZuxZMmffZuxZ6tu+PRqOFi9erL179zrdFxsbq127drU/zsvL06ZNm7RlyxZFRUU5fU5ISMhlQchqtSokJKTHNaalpSk4OLjHz/dmNptN+fn5Sk9PvyyI+ioz9izRt5n6NmPPkjn7NmPPUtd9NzQ09Hpiw6PhKDc396rGbdu2TevWrdNLL72kkSNHdjpu9OjRKikpkdVqVVhYmCTp2LFjGj16dI9rtFgsPv9DZ4YeL2XGniX6NhMz9iyZs28z9ix13rcr3guvX5C9fft2rVmzRr/5zW+UlpZ2xbHDhw/XhAkT9Mwzz6ipqUn/+Mc/9OGHH2ru3LluqhYAAPR3Xr8g+6mnnpLVatWdd97Zvm3SpEnKycmRJC1btkzZ2dlasWKFJOnpp5/Www8/rMmTJys2Nla/+tWvuIwfAABcNa8PRzt37rzi/osh6aLExES99NJLfVkSAADwYV4fjgB8qaq2UcWldTpVblVKQphSk8IVExHk6bIAwKcQjoB+oqq2Uet/d0AHC862b8tMi9OqO7IISADgQl6/IBvABcWldR2CkSQdLDir4tI6D1UEAL6JcAT0E6fKrU63n65wvh0A0DOEI6CfSEkIc7p9SLzz7QCAniEcAf1EalK4MtPiOmzLTItTalJ4J88AAPQEC7KBfiImIkir7shScWmdTldYNSSeq9UAoC8QjoB+JCYiSDERQcoel+DpUgDAZ3FaDQAAwIBwBAAAYEA4AgAAMCAcAQAAGBCOAAAADAhHAAAABoQjAAAAA8IRAACAAeEIAADAgHAEAABgQDgCAAAwIBwBAAAYEI4AAAAMCEcAAAAGhCMAAAADwhEAAIAB4QgAAMCAcAQAAGBAOAIAADAgHAEAABgQjgAAAAwIRwAAAAaEIwAAAAPCEQAAgAHhCAAAwIBwBAAAYEA4AgAAMCAcAQAAGHh9OMrJydHs2bM1ceJEzZgxQ08//bRsNlun49evX6/Zs2dr/PjxevLJJ91YKQAA8AUBni6gK3a7XY8//rjGjh2riooKrVy5UiEhIVq+fLnT8cOGDdMDDzyg119/3c2VAgAAX+D14cgYgpKTkzV79mx98sknnY6/7bbbJEnvvPOOS17fZrNdcaaqP7vYl6/254wZe5bo20x9m7FnyZx9m7Fnqeu+XfF+eH04utS+ffs0ZswYt71eQUGB217LU/Lz8z1dgtuZsWeJvs3EjD1L5uzbjD1Lfdu3R8ORzWaTw+Fwus/Pz08Wi6XDttzcXBUUFGjt2rXuKE+SlJaWpuDgYLe9njvZbDbl5+crPT39svfaV5mxZ4m+zdS3GXuWzNm3GXuWuu67oaGh1xMbHg1Hixcv1t69e53ui42N1a5du9of5+XladOmTdqyZYuioqLcVaIsFovP/9CZocdLmbFnib7NxIw9S+bs24w9S5337Yr3wqPhKDc396rGbdu2TevWrdNLL72kkSNH9nFVAADAzLx+zdH27du1Zs0abd68WWlpaV2Ob21tld1ub19I3dzcLIvFooAAr28VAAB4Aa+/z9FTTz0lq9WqO++8U1lZWcrKytKyZcva9y9btkwvvPBC++P//M//VEZGht5++21t3rxZGRkZ2rhxoydKBwAA/ZDXT6fs3LnzivtzcnI6PP7Vr36lX/3qV31ZEgAA8GFeP3MEAADgToQjAAAAA8IRAACAAeEIAADAgHAEAABgQDgCAAAwIBwBAAAYEI4AAAAMCEcAAAAGhCMAAAADwhEAAIAB4QgAAMCAcAQAAGBAOAIAADAgHAEAABgQjgAAAAwIRwAAAAaEIwAAAAPCEQAAgAHhCAAAwIBwBAAAYEA4AgAAMCAcAQAAGBCOAAAADAhHAAAABoQjAAAAA8IRAACAAeEIAADAgHAEAABgQDgCAAAwIBwBAAAYEI4AAAAMCEcAAAAGhCMAAAADwhEAAIAB4QgAAMDA68NRTk6OZs+erYkTJ2rGjBl6+umnZbPZnI6tqqrSj3/8Y91www2aOHGi5s+fr48++sjNFQMAgP7M68OR3W7X448/rj179ujVV1/Ve++9pxdffNHp2IaGBo0fP15/+MMf9PHHH2vFihW65557VFJS4uaqAQBAfxXg6QK6snz58vbfJycna/bs2frkk0+cjk1JSdHSpUvbH8+aNUvr16/X4cOHlZyc3KPXt9lsnc5U9XcX+/LV/pwxY88SfZupbzP2LJmzbzP2LHXdtyveDz+Hw+Ho9VHcaPny5RozZox+/OMfdzm2vLxcN954o/Ly8jRy5MhuvU5DQ4OOHj3a0zIBAIAHjRs3TsHBwT16rkdnjmw2mzrLZn5+frJYLB225ebmqqCgQGvXru3y2M3NzVq1apW++93vdjsYGaWlpfX4zfV2NptN+fn5Sk9Pv+y99lVm7FmibzP1bcaeJXP2bcaepa77bmhoUEFBQa9ew6PhaPHixdq7d6/TfbGxsdq1a1f747y8PG3atElbtmxRVFTUFY/b0tKie++9V7GxsXr00Ud7VaPFYvH5Hzoz9HgpM/Ys0beZmLFnyZx9m7FnqfO+XfFeeDQc5ebmXtW4bdu2ad26dXrppZe6nAVqaWnRfffdJz8/Pz311FOm/IEBAAA95/ULsrdv3641a9Zo8+bNSktLu+LY1tZWrVq1Si0tLdq4caMGDBjgpioBAICv8Ppw9NRTT8lqterOO+9s3zZp0iTl5ORIkpYtW6bs7GytWLFCBw4c0I4dOxQYGKjJkye3j//5z3+uOXPmuL12AADQ/3h9ONq5c+cV918MSZL0la98RcePH+/rkgAAgA/z+ptAAgAAuBPhCAAAwIBwBAAAYEA4AgAAMCAcAQAAGBCOAAAADAhHAAAABoQjAAAAA8IRAACAAeEIAADAgHAEAABgQDgCAAAwIBwBAAAYEI4AAAAMCEcAAAAGhCMAAACDAE8XYCZVtY0qLq3TqXKrUhLClJoUrpiIIE+XBQAADAhHblJV26j1vzuggwVn27dlpsVp1R1ZBCQAALwIp9XcpLi0rkMwkqSDBWdVXFrnoYoAAIAzhCM3OVVudbr9dIXz7QAAwDMIR26SkhDmdPuQeOfbAQCAZxCO3CQ1KVyZaXEdtmWmxSk1KdxDFQEAAGdYkO0mMRFBWnVHlopL63S6wqoh8VytBgCANyIcuVFMRJBiIoKUPS7B06UAAIBOcFoNAADAgHAEAABgQDgCAAAwIBwBAAAYEI4AAAAMCEcAAAAGhCMAAAADwhEAAIAB4QgAAMCAcAQAAGBAOAIAADDgu9U6YbfbJUmNjY0erqTv2Gw2SVJDQ4MsFouHq3EPM/Ys0beZ+jZjz5I5+zZjz1LXfV/8d/viv+M94edwOBw9frYPq6qq0smTJz1dBgAA6IHhw4crJiamR88lHHWira1NtbW1GjRokPz9OfsIAEB/YLfb1dzcrIiICAUE9OwEGeEIAADAgCkRAAAAA8IRAACAAeEIAADAgHAEAABgQDgCAAAwIBwBAAAYEI4AAAAMCEcAAAAGhCMAAAADwhEAAIAB4chEcnJyNHv2bE2cOFEzZszQ008/3f7txpeqqqrSj3/8Y91www2aOHGi5s+fr48++sjNFbtGd/qWpPXr12v27NkaP368nnzySTdW6jrd7bmsrExLlixRZmamvvGNb+jdd991Y7Wus3v3bi1cuFCTJk3S1KlTuxx/6NAh3XHHHcrKytJNN92knTt3uqFK1+puz0ePHtWdd96p7OxsTZs2TWvXru3Vt5d7Snf6Li0tVVZWVodfY8eO1S9/+Us3Ves63f28JemVV17RN7/5TWVmZmrWrFnav39/H1fpWt3teeHChUpPT+/weXebA6axadMmx6FDhxwtLS2O06dPO2bPnu3YtGmT07FffPGFIycnx3HmzBmHzWZz/PnPf3ZkZmY6Tp8+7eaqe687fTscDscbb7zheP/99x0//OEPHb/+9a/dWKnrdLfnO+64w/Hf//3fjsbGRsdHH33kyMzMdBQVFbmxYtf45z//6XjzzTcdv//97x1f/epXrzi2trbWcf311zvefPNNR1tbm+Nvf/ubIyMjw3Hy5Ek3Vesa3enZ4XA4Zs2a5fj1r3/taG1tdZSWljpmzZrlePXVV91QqWt1t2+jmpoax4QJExz79u3ro+r6Tnf73rp1q+Pb3/624+jRow673e4oKSlxnDlzxg2Vuk53e/7BD37Q659pZo5MZPny5UpPT9eAAQOUnJys2bNnd/o/iJSUFC1dulQJCQny9/fXrFmzlJiYqMOHD7u56t7rTt+SdNttt2nGjBkKDQ11Y5Wu1Z2eT548qfz8fN13330KDAzU1KlTNW3aNOXl5bm56t7LyMjQvHnzNHTo0C7H7t+/X+Hh4Zo3b54sFoumT5+ujIyMftd3d3p2OBwqKSnRvHnzFBAQoMTERE2bNk0nTpxwQ6Wu1Z2+L7V9+3YlJSUpOzu7DyrrW93p226369lnn9Xq1as1duxY+fn5KSkpSQkJCW6o1HV681n3FOHIxPbt26fRo0df1djy8nJ98cUXGjVqVB9X1fe607evuFLPJ06cUFJSksLDw9u3jR07tl/+g9kddrtdDofjsu3Hjx/3QDXu4efnp0WLFmnr1q1qaWnR6dOn9dFHH2n69OmeLs2ttm7dqvnz53u6jD5XXl6usrIynThxQl/72tf0ta99TWvWrFFzc7OnS+tzzzzzjCZPnqzvfOc7PTpdTjjyETabTW1tbU5/OVtrkpubq4KCAi1ZsqTLYzc3N2vVqlX67ne/q5EjR/ZF+T3Wl317K1f3fP78+ctmycLDw3X+/Pk+qb+nutt3V7KyslRdXa2tW7eqtbVV7733nvbv36+mpqY+qL5nXN2zJN14443asWOHMjMzdeONN2ry5MmaMWOGiyvvnb7o+6Ljx4/r2LFjmjdvnmuKdSFX911WViZJ+vDDD/XWW2/ptdde0yeffKJNmza5uvQe64vP+oEHHtBf//pXffjhh1q+fLnuv/9+HTp0qFvHCOjRK8PrLF68WHv37nW6LzY2Vrt27Wp/nJeXp02bNmnLli2Kioq64nFbWlp07733KjY2Vo8++qhLa3aFvurbm7m655CQkMuCkNVqVUhIiOuKdoHu9H01oqKitHHjRq1bt05r165VZmambrnlFg0cONAV5bqEq3uuqanRsmXLtHr1at12222qq6vTQw89pCeffFIPPvigK0p2CVf3bbR161ZNmzbNK08tubrvoKAgSdKyZcsUGRnZ/hpbtmzRvffe26taXaUvPutrr722/fezZs3Sjh079O677yojI+Oqj0E48hG5ublXNW7btm1at26dXnrppS5ngVpaWnTffffJz89PTz31lCwWiytKdam+6Nvbubrn0aNHq6SkRFarVWFhYZKkY8eOed2px6vtuzuuu+46vf766+2Pb7/9di1YsMDlr9NTru751KlTkqTvfe97kqSYmBjNnz9fGzZs8Kpw1BeftSS1trZq27Zteuyxx/rk+L3l6r6HDx/uVWHfmb76rI38/f2dnkK/4nP6qBZ4oe3bt2vNmjX6zW9+o7S0tCuObW1t1apVq9TS0qJnn31WAwYMcFOVrtedvqULvTc3N8tms8lms6m5uVltbW1uqNR1utPz8OHDNWHCBD3zzDNqamrSP/7xD3344YeaO3eum6p1HbvdrubmZrW2tkq6cEq4paWl0/FHjhxRS0uLzp8/rxdeeEHV1dVeebrlSrrTc2pqqiwWi7Zu3Sqbzaaamhq99dZbGjNmjDtLdonuftaS9N5778nhcGjmzJnuKLFPdKfvoKAg3XrrrXrxxRdltVpVVVWll19+ud/1352e6+rq9Le//U1NTU2y2WzasWOH3nnnnW737OfobpxCvzVz5kyVl5d3+J/EpEmTlJOTI+nC1Gt2drZWrFihvXv3auHChQoMDJS//5cZ+uc//7nmzJnj9tp7ozt9S9JDDz2kN998s8MxfvSjH+mee+5xX9G91N2ey8rK9PDDD2v//v2KjY3VT37yE910000eqb039uzZo0WLFnXYlpyc3L4g89K+H3zwQe3cuVN2u12TJ0/WI488opSUFLfX3Rvd7XnPnj36n//5HxUWFmrgwIGaMmWKHn30UUVHR7u99t7obt+StGLFCqWkpOiRRx5xa62u1N2+6+vr9bOf/Uw7d+5UcHCwbr31Vt1///1eP6Nk1J2eq6urtXz5chUWFsrf31/Dhg3T3Xff3e2/zwhHAAAABpxWAwAAMCAcAQAAGBCOAAAADAhHAAAABoQjAAAAA8IRAACAAeEIAADAgHAEAABgQDgCAAAwIBwB6JcWLlyoMWPG6KOPPuqw/YEHHtBDDz3koaoA+ALCEYB+a9CgQVq/fr2nywDgYwhHAPqtuXPnqrCwUH/961+d7q+srNS9996rrKwsXXfddXr44YfV0NDQvn/hwoVat26d/uu//ktZWVmaOXOm/vjHP3Y4xqeffqqFCxcqIyNDM2fO1LPPPqu2trY+7QuAZxGOAPRb0dHRWrRokZ555hnZ7fbL9v/kJz9RWVmZcnNztXHjRn388cd64oknOox57bXXNGLECL311lu67bbbtHr1alVVVUmSzp07pyVLlmj69Onatm2bnnjiCW3fvl2bN292S38APINwBKBfW7p0qc6cOXPZjE9hYaF27dqlJ554QhMmTFB2drYeffRRvfHGG7Jare3jJk6cqMWLF2vYsGFauXKl/P39dejQIUnSK6+8osmTJ+uuu+7SsGHDNHnyZN1zzz16/fXX3dojAPcK8HQBANAb4eHhWrJkiZ577jndcsst7duLi4sVEhKiUaNGtW/LyspSW1ubvvjiC11zzTWSpLS0tPb9AQEBioqKap85Kigo0M6dO5WVldU+xmazqa2tTXa7Xf7+/P8S8EWEIwD93qJFi/Tyyy/rzTffvOI4Pz+/y7YFBARcNsbhcEiSGhoa9K1vfUs//OEPL3sewQjwXYQjAP1eSEiI7r77bm3YsEEZGRkKCAhQamqqzp8/r88++6x99mj//v0KCAjQ0KFDr+q4Y8eO1d///ncNGzasL8sH4GX4rw8An/D9739fDodD77//viRp5MiRmjZtmh5++GEdPnxYn3zyidasWaP58+crLCzsqo5555136tSpU3r00Ud17NgxFRUV6U9/+pOef/75PuwEgKcRjgD4hEGDBmnlypVqbm5u37Z27VolJCRo4cKFuvvuuzVp0iStXr36qo+ZmJio3/72tyorK9P3v/99fec739HmzZuVlJTUFy0A8BJ+josn1wEAAMDMEQAAgBHhCAAAwIBwBAAAYEA4AgAAMCAcAQAAGBCOAAAADAhHAAAABoQjAAAAA8IRAACAAeEIAADAgHAEAABg8P8DcFbx9Tla1xgAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x = old_spcc.iloc[:, 2:].sum(axis= 1)[-500:],y = new_spcc.iloc[:, 2:].sum(axis= 1)[-500:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f63e30db550>]"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(old_spcc.iloc[:, 2:].sum(axis= 1) - new_spcc.iloc[:, 2:].sum(axis= 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([2001017.32939279]), array([2001017.32939279]))"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a*100000000000, b*100000000000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dal80_AmmoniumSulfate</th>\n",
       "      <th>dal80_CStarve</th>\n",
       "      <th>dal80_Glutamine</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>edge</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>YOL077C_YEL026W</th>\n",
       "      <td>0.00011</td>\n",
       "      <td>0.009698</td>\n",
       "      <td>0.00451</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 dal80_AmmoniumSulfate  dal80_CStarve  dal80_Glutamine\n",
       "edge                                                                  \n",
       "YOL077C_YEL026W                0.00011       0.009698          0.00451"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_spcc[(new_spcc['gene1']=='YOL077C') & (new_spcc['gene2']=='YEL026W')].loc[:,['dal80_AmmoniumSulfate','dal80_CStarve','dal80_Glutamine']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dal80_AmmoniumSulfate</th>\n",
       "      <th>dal80_CStarve</th>\n",
       "      <th>dal80_Glutamine</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>edge</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>YOL077C_YEL026W</th>\n",
       "      <td>0.00011</td>\n",
       "      <td>0.009698</td>\n",
       "      <td>0.00451</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 dal80_AmmoniumSulfate  dal80_CStarve  dal80_Glutamine\n",
       "edge                                                                  \n",
       "YOL077C_YEL026W                0.00011       0.009698          0.00451"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "old_spcc[(old_spcc['gene1']=='YOL077C') & (old_spcc['gene2']=='YEL026W')].loc[:,['dal80_AmmoniumSulfate','dal80_CStarve','dal80_Glutamine']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Check inferelator SPCC original networks\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "File ../../old_inferelator_spcc_dal80.h5 does not exist",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[106], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m old_dal80 \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_hdf\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m../../old_inferelator_spcc_dal80.h5\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m      3\u001b[0m new_dal80 \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mread_hdf(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m../results/pseudobulk_nonzero/networks/spcc/inferelator_spcc_dal80.h5\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
      "File \u001b[0;32m/opt/conda/envs/bonobo-env/lib/python3.9/site-packages/pandas/io/pytables.py:424\u001b[0m, in \u001b[0;36mread_hdf\u001b[0;34m(path_or_buf, key, mode, errors, where, start, stop, columns, iterator, chunksize, **kwargs)\u001b[0m\n\u001b[1;32m    421\u001b[0m     exists \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m    423\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m exists:\n\u001b[0;32m--> 424\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mFileNotFoundError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFile \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mpath_or_buf\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m does not exist\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    426\u001b[0m store \u001b[38;5;241m=\u001b[39m HDFStore(path_or_buf, mode\u001b[38;5;241m=\u001b[39mmode, errors\u001b[38;5;241m=\u001b[39merrors, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m    427\u001b[0m \u001b[38;5;66;03m# can't auto open/close if we are using an iterator\u001b[39;00m\n\u001b[1;32m    428\u001b[0m \u001b[38;5;66;03m# so delegate to the iterator\u001b[39;00m\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: File ../../old_inferelator_spcc_dal80.h5 does not exist"
     ]
    }
   ],
   "source": [
    "old_dal80 = pd.read_hdf('../../old_inferelator_spcc_dal80.h5')\n",
    "\n",
    "new_dal80 = pd.read_hdf('../results/pseudobulk_nonzero/networks/spcc/inferelator_spcc_dal80.h5')\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gene1</th>\n",
       "      <th>gene2</th>\n",
       "      <th>dal80_AmmoniumSulfate</th>\n",
       "      <th>dal80_CStarve</th>\n",
       "      <th>dal80_Glutamine</th>\n",
       "      <th>dal80_MinimalEtOH</th>\n",
       "      <th>dal80_MinimalGlucose</th>\n",
       "      <th>dal80_Proline</th>\n",
       "      <th>dal80_Urea</th>\n",
       "      <th>dal80_YPD</th>\n",
       "      <th>dal80_YPDDiauxic</th>\n",
       "      <th>dal80_YPDRapa</th>\n",
       "      <th>dal80_YPEtOH</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>YDL248W</td>\n",
       "      <td>YDL247W</td>\n",
       "      <td>-0.001124</td>\n",
       "      <td>-0.000478</td>\n",
       "      <td>-0.000866</td>\n",
       "      <td>0.000477</td>\n",
       "      <td>0.001642</td>\n",
       "      <td>0.000518</td>\n",
       "      <td>0.001180</td>\n",
       "      <td>0.005264</td>\n",
       "      <td>-0.000232</td>\n",
       "      <td>0.000220</td>\n",
       "      <td>-0.000477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>YDL248W</td>\n",
       "      <td>YDL246C</td>\n",
       "      <td>-0.001022</td>\n",
       "      <td>-0.000435</td>\n",
       "      <td>-0.000787</td>\n",
       "      <td>0.000434</td>\n",
       "      <td>0.001492</td>\n",
       "      <td>0.000471</td>\n",
       "      <td>0.001073</td>\n",
       "      <td>0.004784</td>\n",
       "      <td>-0.000211</td>\n",
       "      <td>0.000200</td>\n",
       "      <td>-0.000434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>YDL248W</td>\n",
       "      <td>YDL245C</td>\n",
       "      <td>-0.001708</td>\n",
       "      <td>-0.000727</td>\n",
       "      <td>-0.001316</td>\n",
       "      <td>0.000725</td>\n",
       "      <td>0.002495</td>\n",
       "      <td>0.000787</td>\n",
       "      <td>0.001793</td>\n",
       "      <td>-0.022268</td>\n",
       "      <td>-0.000352</td>\n",
       "      <td>-0.000828</td>\n",
       "      <td>-0.000725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>YDL248W</td>\n",
       "      <td>YDL244W</td>\n",
       "      <td>-0.008664</td>\n",
       "      <td>0.001394</td>\n",
       "      <td>-0.003244</td>\n",
       "      <td>-0.008224</td>\n",
       "      <td>0.004424</td>\n",
       "      <td>0.003991</td>\n",
       "      <td>0.009094</td>\n",
       "      <td>0.032012</td>\n",
       "      <td>0.000936</td>\n",
       "      <td>-0.000479</td>\n",
       "      <td>0.005023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>YDL248W</td>\n",
       "      <td>YDL243C</td>\n",
       "      <td>-0.009982</td>\n",
       "      <td>0.000469</td>\n",
       "      <td>-0.003150</td>\n",
       "      <td>-0.014168</td>\n",
       "      <td>0.005810</td>\n",
       "      <td>0.005861</td>\n",
       "      <td>0.009665</td>\n",
       "      <td>0.034040</td>\n",
       "      <td>0.001860</td>\n",
       "      <td>-0.001599</td>\n",
       "      <td>0.003909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21251935</th>\n",
       "      <td>Q0275</td>\n",
       "      <td>KANMX</td>\n",
       "      <td>-0.002556</td>\n",
       "      <td>0.005696</td>\n",
       "      <td>0.000063</td>\n",
       "      <td>0.000328</td>\n",
       "      <td>0.103401</td>\n",
       "      <td>0.004276</td>\n",
       "      <td>-0.009330</td>\n",
       "      <td>-0.006982</td>\n",
       "      <td>0.001800</td>\n",
       "      <td>0.001693</td>\n",
       "      <td>-0.003546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21251936</th>\n",
       "      <td>Q0275</td>\n",
       "      <td>NATMX</td>\n",
       "      <td>-0.000581</td>\n",
       "      <td>0.005040</td>\n",
       "      <td>-0.001666</td>\n",
       "      <td>0.002117</td>\n",
       "      <td>0.109617</td>\n",
       "      <td>0.005713</td>\n",
       "      <td>-0.002651</td>\n",
       "      <td>-0.036834</td>\n",
       "      <td>0.001851</td>\n",
       "      <td>-0.002424</td>\n",
       "      <td>-0.002676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21251937</th>\n",
       "      <td>RPM1</td>\n",
       "      <td>KANMX</td>\n",
       "      <td>-0.001120</td>\n",
       "      <td>0.002497</td>\n",
       "      <td>0.000028</td>\n",
       "      <td>0.000144</td>\n",
       "      <td>-0.004581</td>\n",
       "      <td>0.001874</td>\n",
       "      <td>-0.004090</td>\n",
       "      <td>-0.003060</td>\n",
       "      <td>0.000789</td>\n",
       "      <td>0.000742</td>\n",
       "      <td>-0.001554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21251938</th>\n",
       "      <td>RPM1</td>\n",
       "      <td>NATMX</td>\n",
       "      <td>-0.000254</td>\n",
       "      <td>0.002209</td>\n",
       "      <td>-0.000730</td>\n",
       "      <td>0.000928</td>\n",
       "      <td>-0.004857</td>\n",
       "      <td>0.002504</td>\n",
       "      <td>-0.001162</td>\n",
       "      <td>-0.016146</td>\n",
       "      <td>0.000811</td>\n",
       "      <td>-0.001062</td>\n",
       "      <td>-0.001173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21251939</th>\n",
       "      <td>KANMX</td>\n",
       "      <td>NATMX</td>\n",
       "      <td>0.001517</td>\n",
       "      <td>0.027203</td>\n",
       "      <td>-0.000067</td>\n",
       "      <td>0.000710</td>\n",
       "      <td>0.034407</td>\n",
       "      <td>0.023005</td>\n",
       "      <td>0.010224</td>\n",
       "      <td>0.023831</td>\n",
       "      <td>0.002978</td>\n",
       "      <td>-0.004196</td>\n",
       "      <td>0.009701</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>21251940 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            gene1    gene2  dal80_AmmoniumSulfate  dal80_CStarve  \\\n",
       "0         YDL248W  YDL247W              -0.001124      -0.000478   \n",
       "1         YDL248W  YDL246C              -0.001022      -0.000435   \n",
       "2         YDL248W  YDL245C              -0.001708      -0.000727   \n",
       "3         YDL248W  YDL244W              -0.008664       0.001394   \n",
       "4         YDL248W  YDL243C              -0.009982       0.000469   \n",
       "...           ...      ...                    ...            ...   \n",
       "21251935    Q0275    KANMX              -0.002556       0.005696   \n",
       "21251936    Q0275    NATMX              -0.000581       0.005040   \n",
       "21251937     RPM1    KANMX              -0.001120       0.002497   \n",
       "21251938     RPM1    NATMX              -0.000254       0.002209   \n",
       "21251939    KANMX    NATMX               0.001517       0.027203   \n",
       "\n",
       "          dal80_Glutamine  dal80_MinimalEtOH  dal80_MinimalGlucose  \\\n",
       "0               -0.000866           0.000477              0.001642   \n",
       "1               -0.000787           0.000434              0.001492   \n",
       "2               -0.001316           0.000725              0.002495   \n",
       "3               -0.003244          -0.008224              0.004424   \n",
       "4               -0.003150          -0.014168              0.005810   \n",
       "...                   ...                ...                   ...   \n",
       "21251935         0.000063           0.000328              0.103401   \n",
       "21251936        -0.001666           0.002117              0.109617   \n",
       "21251937         0.000028           0.000144             -0.004581   \n",
       "21251938        -0.000730           0.000928             -0.004857   \n",
       "21251939        -0.000067           0.000710              0.034407   \n",
       "\n",
       "          dal80_Proline  dal80_Urea  dal80_YPD  dal80_YPDDiauxic  \\\n",
       "0              0.000518    0.001180   0.005264         -0.000232   \n",
       "1              0.000471    0.001073   0.004784         -0.000211   \n",
       "2              0.000787    0.001793  -0.022268         -0.000352   \n",
       "3              0.003991    0.009094   0.032012          0.000936   \n",
       "4              0.005861    0.009665   0.034040          0.001860   \n",
       "...                 ...         ...        ...               ...   \n",
       "21251935       0.004276   -0.009330  -0.006982          0.001800   \n",
       "21251936       0.005713   -0.002651  -0.036834          0.001851   \n",
       "21251937       0.001874   -0.004090  -0.003060          0.000789   \n",
       "21251938       0.002504   -0.001162  -0.016146          0.000811   \n",
       "21251939       0.023005    0.010224   0.023831          0.002978   \n",
       "\n",
       "          dal80_YPDRapa  dal80_YPEtOH  \n",
       "0              0.000220     -0.000477  \n",
       "1              0.000200     -0.000434  \n",
       "2             -0.000828     -0.000725  \n",
       "3             -0.000479      0.005023  \n",
       "4             -0.001599      0.003909  \n",
       "...                 ...           ...  \n",
       "21251935       0.001693     -0.003546  \n",
       "21251936      -0.002424     -0.002676  \n",
       "21251937       0.000742     -0.001554  \n",
       "21251938      -0.001062     -0.001173  \n",
       "21251939      -0.004196      0.009701  \n",
       "\n",
       "[21251940 rows x 13 columns]"
      ]
     },
     "execution_count": 369,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_dal80"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gene1</th>\n",
       "      <th>gene2</th>\n",
       "      <th>dal80_AmmoniumSulfate</th>\n",
       "      <th>dal80_CStarve</th>\n",
       "      <th>dal80_Glutamine</th>\n",
       "      <th>dal80_MinimalEtOH</th>\n",
       "      <th>dal80_MinimalGlucose</th>\n",
       "      <th>dal80_Proline</th>\n",
       "      <th>dal80_Urea</th>\n",
       "      <th>dal80_YPD</th>\n",
       "      <th>dal80_YPDDiauxic</th>\n",
       "      <th>dal80_YPDRapa</th>\n",
       "      <th>dal80_YPEtOH</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>YDL248W</td>\n",
       "      <td>YDL247W</td>\n",
       "      <td>-0.001124</td>\n",
       "      <td>-0.000478</td>\n",
       "      <td>-0.000866</td>\n",
       "      <td>0.000477</td>\n",
       "      <td>0.001642</td>\n",
       "      <td>0.000518</td>\n",
       "      <td>0.001180</td>\n",
       "      <td>0.005264</td>\n",
       "      <td>-0.000232</td>\n",
       "      <td>0.000220</td>\n",
       "      <td>-0.000477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>YDL248W</td>\n",
       "      <td>YDL246C</td>\n",
       "      <td>-0.001022</td>\n",
       "      <td>-0.000435</td>\n",
       "      <td>-0.000787</td>\n",
       "      <td>0.000434</td>\n",
       "      <td>0.001492</td>\n",
       "      <td>0.000471</td>\n",
       "      <td>0.001073</td>\n",
       "      <td>0.004784</td>\n",
       "      <td>-0.000211</td>\n",
       "      <td>0.000200</td>\n",
       "      <td>-0.000434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>YDL248W</td>\n",
       "      <td>YDL245C</td>\n",
       "      <td>-0.001708</td>\n",
       "      <td>-0.000727</td>\n",
       "      <td>-0.001316</td>\n",
       "      <td>0.000725</td>\n",
       "      <td>0.002495</td>\n",
       "      <td>0.000787</td>\n",
       "      <td>0.001793</td>\n",
       "      <td>-0.022268</td>\n",
       "      <td>-0.000352</td>\n",
       "      <td>-0.000828</td>\n",
       "      <td>-0.000725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>YDL248W</td>\n",
       "      <td>YDL244W</td>\n",
       "      <td>-0.008664</td>\n",
       "      <td>0.001394</td>\n",
       "      <td>-0.003244</td>\n",
       "      <td>-0.008224</td>\n",
       "      <td>0.004424</td>\n",
       "      <td>0.003991</td>\n",
       "      <td>0.009094</td>\n",
       "      <td>0.032012</td>\n",
       "      <td>0.000936</td>\n",
       "      <td>-0.000479</td>\n",
       "      <td>0.005023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>YDL248W</td>\n",
       "      <td>YDL243C</td>\n",
       "      <td>-0.009982</td>\n",
       "      <td>0.000469</td>\n",
       "      <td>-0.003150</td>\n",
       "      <td>-0.014168</td>\n",
       "      <td>0.005810</td>\n",
       "      <td>0.005861</td>\n",
       "      <td>0.009665</td>\n",
       "      <td>0.034040</td>\n",
       "      <td>0.001860</td>\n",
       "      <td>-0.001599</td>\n",
       "      <td>0.003909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21251935</th>\n",
       "      <td>Q0275</td>\n",
       "      <td>KANMX</td>\n",
       "      <td>-0.002556</td>\n",
       "      <td>0.005696</td>\n",
       "      <td>0.000063</td>\n",
       "      <td>0.000328</td>\n",
       "      <td>0.103401</td>\n",
       "      <td>0.004276</td>\n",
       "      <td>-0.009330</td>\n",
       "      <td>-0.006982</td>\n",
       "      <td>0.001800</td>\n",
       "      <td>0.001693</td>\n",
       "      <td>-0.003546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21251936</th>\n",
       "      <td>Q0275</td>\n",
       "      <td>NATMX</td>\n",
       "      <td>-0.000581</td>\n",
       "      <td>0.005040</td>\n",
       "      <td>-0.001666</td>\n",
       "      <td>0.002117</td>\n",
       "      <td>0.109617</td>\n",
       "      <td>0.005713</td>\n",
       "      <td>-0.002651</td>\n",
       "      <td>-0.036834</td>\n",
       "      <td>0.001851</td>\n",
       "      <td>-0.002424</td>\n",
       "      <td>-0.002676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21251937</th>\n",
       "      <td>RPM1</td>\n",
       "      <td>KANMX</td>\n",
       "      <td>-0.001120</td>\n",
       "      <td>0.002497</td>\n",
       "      <td>0.000028</td>\n",
       "      <td>0.000144</td>\n",
       "      <td>-0.004581</td>\n",
       "      <td>0.001874</td>\n",
       "      <td>-0.004090</td>\n",
       "      <td>-0.003060</td>\n",
       "      <td>0.000789</td>\n",
       "      <td>0.000742</td>\n",
       "      <td>-0.001554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21251938</th>\n",
       "      <td>RPM1</td>\n",
       "      <td>NATMX</td>\n",
       "      <td>-0.000254</td>\n",
       "      <td>0.002209</td>\n",
       "      <td>-0.000730</td>\n",
       "      <td>0.000928</td>\n",
       "      <td>-0.004857</td>\n",
       "      <td>0.002504</td>\n",
       "      <td>-0.001162</td>\n",
       "      <td>-0.016146</td>\n",
       "      <td>0.000811</td>\n",
       "      <td>-0.001062</td>\n",
       "      <td>-0.001173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21251939</th>\n",
       "      <td>KANMX</td>\n",
       "      <td>NATMX</td>\n",
       "      <td>0.001517</td>\n",
       "      <td>0.027203</td>\n",
       "      <td>-0.000067</td>\n",
       "      <td>0.000710</td>\n",
       "      <td>0.034407</td>\n",
       "      <td>0.023005</td>\n",
       "      <td>0.010224</td>\n",
       "      <td>0.023831</td>\n",
       "      <td>0.002978</td>\n",
       "      <td>-0.004196</td>\n",
       "      <td>0.009701</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>21251940 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            gene1    gene2  dal80_AmmoniumSulfate  dal80_CStarve  \\\n",
       "0         YDL248W  YDL247W              -0.001124      -0.000478   \n",
       "1         YDL248W  YDL246C              -0.001022      -0.000435   \n",
       "2         YDL248W  YDL245C              -0.001708      -0.000727   \n",
       "3         YDL248W  YDL244W              -0.008664       0.001394   \n",
       "4         YDL248W  YDL243C              -0.009982       0.000469   \n",
       "...           ...      ...                    ...            ...   \n",
       "21251935    Q0275    KANMX              -0.002556       0.005696   \n",
       "21251936    Q0275    NATMX              -0.000581       0.005040   \n",
       "21251937     RPM1    KANMX              -0.001120       0.002497   \n",
       "21251938     RPM1    NATMX              -0.000254       0.002209   \n",
       "21251939    KANMX    NATMX               0.001517       0.027203   \n",
       "\n",
       "          dal80_Glutamine  dal80_MinimalEtOH  dal80_MinimalGlucose  \\\n",
       "0               -0.000866           0.000477              0.001642   \n",
       "1               -0.000787           0.000434              0.001492   \n",
       "2               -0.001316           0.000725              0.002495   \n",
       "3               -0.003244          -0.008224              0.004424   \n",
       "4               -0.003150          -0.014168              0.005810   \n",
       "...                   ...                ...                   ...   \n",
       "21251935         0.000063           0.000328              0.103401   \n",
       "21251936        -0.001666           0.002117              0.109617   \n",
       "21251937         0.000028           0.000144             -0.004581   \n",
       "21251938        -0.000730           0.000928             -0.004857   \n",
       "21251939        -0.000067           0.000710              0.034407   \n",
       "\n",
       "          dal80_Proline  dal80_Urea  dal80_YPD  dal80_YPDDiauxic  \\\n",
       "0              0.000518    0.001180   0.005264         -0.000232   \n",
       "1              0.000471    0.001073   0.004784         -0.000211   \n",
       "2              0.000787    0.001793  -0.022268         -0.000352   \n",
       "3              0.003991    0.009094   0.032012          0.000936   \n",
       "4              0.005861    0.009665   0.034040          0.001860   \n",
       "...                 ...         ...        ...               ...   \n",
       "21251935       0.004276   -0.009330  -0.006982          0.001800   \n",
       "21251936       0.005713   -0.002651  -0.036834          0.001851   \n",
       "21251937       0.001874   -0.004090  -0.003060          0.000789   \n",
       "21251938       0.002504   -0.001162  -0.016146          0.000811   \n",
       "21251939       0.023005    0.010224   0.023831          0.002978   \n",
       "\n",
       "          dal80_YPDRapa  dal80_YPEtOH  \n",
       "0              0.000220     -0.000477  \n",
       "1              0.000200     -0.000434  \n",
       "2             -0.000828     -0.000725  \n",
       "3             -0.000479      0.005023  \n",
       "4             -0.001599      0.003909  \n",
       "...                 ...           ...  \n",
       "21251935       0.001693     -0.003546  \n",
       "21251936      -0.002424     -0.002676  \n",
       "21251937       0.000742     -0.001554  \n",
       "21251938      -0.001062     -0.001173  \n",
       "21251939      -0.004196      0.009701  \n",
       "\n",
       "[21251940 rows x 13 columns]"
      ]
     },
     "execution_count": 370,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "old_dal80"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "21251940\n",
      "21111017\n",
      "21251940\n",
      "21067832\n",
      "21251940\n",
      "21091384\n",
      "21251940\n",
      "21111236\n",
      "21251940\n",
      "21102423\n",
      "21251940\n",
      "21128424\n",
      "21251940\n",
      "21120841\n",
      "21251940\n",
      "21126333\n",
      "21251940\n",
      "21124871\n",
      "21251940\n",
      "21121257\n",
      "21251940\n",
      "21119502\n"
     ]
    }
   ],
   "source": [
    "for iii in np.arange(len(new_dal80.columns)-2):\n",
    "    print((np.isclose(new_dal80.iloc[:,iii+2].values, old_dal80.iloc[:,iii+2].values)).sum())\n",
    "    print((new_dal80.iloc[:,iii+2].values==old_dal80.iloc[:,iii+2].values).sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_sum = new_dal80.iloc[:,2:].sum(axis = 1).sort_values()\n",
    "old_sum = old_dal80.iloc[:,2:].sum(axis = 1).sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1000"
      ]
     },
     "execution_count": 392,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(set(new_sum.index[:1000].tolist()).intersection(old_sum.index[:1000].tolist()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='None', ylabel='None'>"
      ]
     },
     "execution_count": 390,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x = new_sum.index[:1000], y = old_sum.index[:1000])\n",
    "sns.scatterplot(x = new_sum.index[-1000:], y = old_sum.index[-1000:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='Count'>"
      ]
     },
     "execution_count": 383,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot((new_dal80.iloc[:,2:].values-old_dal80.iloc[:,2:].values).flatten())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.00112426, -0.00102178, -0.00170829, ..., -0.00112031,\n",
       "       -0.00025446,  0.00151703])"
      ]
     },
     "execution_count": 375,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_dal80.iloc[:,iii+2].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.18"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
